Discover Computer Vision Projects Efficiently

I need help annotating images with segmentation masks, focusing on approximately 250-300 images with 4 class types. The task requires precision and attention to detail to ensure accurate annotation. The ideal candidate should have experience in image processing and editing, and be familiar with tools like Roboflow, Label Studio and other processing tools.

We build mobile apps for car enthusiasts. We're building a feature that
lets users visualize modifications on a car photo — realistically and
deterministically — WITHOUT generative AI (no Stable Diffusion, no FLUX,
no diffusion models). We want consistent, repeatable results, which is
why we're going the classical CV route instead of generative models.

THE MODIFICATIONS WE NEED:

1. Color change
- Change the car's body paint to any target color
- Must preserve original reflections, highlights, shadows and metallic
finish (look like a real photo, not a flat repaint)
- Only the body changes; windows, tires, background, grille, lights
stay untouched

2. Wheel / rim replacement
- Replace the car's wheels with a different rim, given as a separate
rim image
- Must match the perspective of the wheel in the photo (rim warped
correctly onto the elliptical wheel area)
- Handle lighting, and ideally the brake caliper / wheel gaps
realistically

3. Hood replacement
- Replace the hood panel with a different hood design (given as a
separate image), warped to the car's perspective, with matching
lighting

4. Additive parts (spoilers, body kits) — EXPLORATION
- Adding parts that don't exist in the original photo (e.g. a rear
spoiler) is harder with pure 2D CV. We're open to your honest
assessment: fixed-angle PNG overlay, 3D, or another approach.
Tell us what's realistic. This is a discussion, not a hard
requirement for the trial.

EXPECTED APPROACH (open to your input):
- Segmentation / masking
- Color manipulation in HSV or LAB space (hue shift, luminance preserved)
- Homography / perspective warping for wheels and hood
- Poisson blending / seamless compositing
- Luminance transfer to keep original lighting on replaced parts

Tech: Python, OpenCV. Strong experience in image segmentation,
perspective transforms and image compositing is a must.

HOW WE WORK:
We start with a small PAID TRIAL focused on items 1 and 2 (color + wheel).
We'll send you 2-3 real car photos, target colors and a rim image. You
deliver the results plus a short note on your approach. If we're happy
with the quality, we move to the full project — we have a roadmap of
car-editing features and expect ongoing work.

PLEASE INCLUDE IN YOUR REPLY:
- A relevant example from your past work (image compositing / perspective
warping / color editing preferred)
- Confirmation you can do this WITHOUT generative AI
- Your honest take on which of the 4 items are realistic with classical CV
- Your rate and rough timeline for the trial

Phasing out Typebot and creating our own chatbot so that we don't need to rely on Typebot. So that the conversations can be properly remembered in realtime.

Project overview

We are looking for an experienced computer-vision engineer to develop a proof-of-concept system for virtual advertising in recorded football broadcast footage.

The initial prototype should replace advertising shown on physical LED perimeter boards with new supplied creative, while keeping the replacement correctly positioned as the broadcast camera moves.

This is an early-stage technical feasibility project. The first version will work with recorded footage rather than a live broadcast feed.

Initial prototype requirements

Using a short sample of football broadcast footage, the system should:

Identify and track the visible LED perimeter boards.
Replace the existing board content with supplied video or animated graphics.
Maintain correct scale, perspective and positioning during camera movement.
Mask players, officials and other foreground objects when they pass in front of the advertising.
Produce a clean exported 1080p video.
Use automated or semi-automated computer vision rather than manual frame-by-frame editing.
Be capable of being adapted to additional footage and stadium configurations.

The successful freelancer must provide the complete source code, documentation and setup instructions.

Required experience

Applicants should have strong practical experience in several of the following areas:

OpenCV
Python and/or C++
PyTorch, TensorFlow or similar frameworks
Camera calibration
Homography and perspective transformation
Semantic or instance segmentation
Object detection and tracking
Video compositing
FFmpeg or GStreamer
CUDA or GPU optimisation
Real-time or near-real-time video processing

Experience with broadcast graphics, augmented reality, virtual production, sports analytics, Unreal Engine, NDI or SDI workflows would be useful but is not essential.

Initial deliverable

The first paid milestone will involve approximately 30–60 seconds of supplied football footage.

The expected deliverable is:

Replacement of the visible LED advertising.
Stable tracking during camera pan, tilt and zoom.
Foreground masking when players cross the advertising area.
An exported demonstration video.
Complete source code.
Instructions explaining how to run the system with replacement creative.

This is not a video-editing or manual rotoscoping project. We are looking for a technically reusable computer-vision solution.

Potential future work

If the prototype is successful, further work may include:

Longer football broadcasts.
Automated stadium and board calibration.
Virtual 3D advertising carpets.
Support for different stadiums and camera positions.
An operator interface.
GPU optimisation.
Near-live or live video processing.
Multiple regional advertising outputs.

There is potential for a longer-term technical relationship for the right person or team.

Application questions

Please answer the following questions in your proposal:

Describe a relevant project where you anchored graphics or virtual objects to footage from a moving camera.
How would you track the geometry of LED perimeter boards during camera movement?
How would you mask players passing in front of the replacement advertising?
Would you use classical computer vision, machine learning or a combination of both?
How would you minimise manual calibration or rotoscoping?
What processing speed would you expect to achieve at 1080p?
What additional development would be required to make the system operate live?
Which parts of your previous work can you demonstrate on a video call?
Are you personally completing the work, or would any part be subcontracted?

Generic AI proposals will not be considered. Please explain your proposed technical approach and include examples of directly relevant work.

Commercial terms

The project will be structured through fixed-price milestones.

The client must receive:

Ownership of all bespoke source code following payment.
Access to the code throughout development through a client-controlled repository.
A list of all third-party libraries, models and licences.
Full disclosure of any existing proprietary components.
Documentation sufficient for another engineer to run and review the system.

The supplied footage and commercial information must remain confidential and may not be used in portfolios or demonstrations without written permission.

Please include:

Your proposed budget for the initial prototype.
Estimated delivery schedule.
Recommended technology stack.
Any assumptions or technical limitations.

Creating our own custom chatbot so that we are no longer reliant on Typebot.

We are looking for an experienced Unity and computer vision developer to help evaluate and improve a real-time human segmentation prototype for an indoor interactive display.

The application already has its primary Unity workflow in place. Your responsibility will be limited to the camera and segmentation pipeline, including:
- Real-time full-body person segmentation
- Clean edges around hair and clothing
- Multiple-person and occlusion handling
- Stability under changing lighting and movement
- Unity/C# integration
- GPU optimization targeting 24–30 FPS
- Evaluation of ONNX, TensorRT, or similar inference options

The initial engagement will include reviewing the technical requirements, helping with solution planning, and participating in a technical client discussion as part of our development team. If the project proceeds, you should also be available to implement the proof of concept.

Please include:
- Relevant Unity/computer vision experience
- Segmentation or video-matting projects
- Models or frameworks you would consider
- Your availability for a technical call
- Estimated budget

Project Overview

We are currently recruiting contributors across the United States for a paid smartphone-based First-Person (POV) Video Collection Project.

The project involves recording natural daily household activities from a first-person perspective to help improve next-generation AI and Computer Vision models.

No prior experience is required. If you meet the device requirements and can follow simple recording guidelines, we'd love to hear from you!
Approved Devices

You must own one of the following approved smartphones:

Apple
iPhone 12 or newer
Samsung
Galaxy S22, S23, or S24 Series
FE models are NOT supported
Google Pixel 6 to 8

Equipment Required
Approved Smartphone
Head-mounted phone holder (Head Strap)
Stable Wi-Fi / Internet Connection
Recording Activities

Examples include:

Cooking
Washing dishes
Laundry
Folding clothes
Cleaning kitchen
Cleaning bathroom
Grocery unpacking
Organizing rooms
Taking out trash
Home organization
Other everyday household activities
Recording Guidelines
Record from a first-person (POV) perspective.
Your face is not required.
Audio is not required.
Each recording must be more than 2 minutes and no longer than 30 minutes.
Perform activities naturally while following the provided guidelines.
Upload recordings regularly.

We're building NeoFocus, an AI-powered IoT device paired with a mobile/web app that helps foster carers and parents detect early signs of mental health issues (PTSD, anxiety, depression, ADHD) in children through facial expression and behavioral pattern analysis. We're looking for an engineer to build a working prototype: an intelligent camera (IoT) device that captures images, an image-processing pipeline using SVM/CNN classifiers to detect emotional states, and a companion mobile app that displays alerts and history to caregivers. Experience with computer vision, embedded/IoT hardware integration, and mobile app development (iOS/Android) is required. Full business plan and technical requirements available to share with the right candidate.

We are a stealth-mode startup building an AI-powered automation platform for the e-commerce and media technology industry.

We're looking for a Senior Full-Stack AI Engineer to develop a standalone AI media processing engine as part of our MVP. This project has a well-defined scope, detailed technical specifications, and the potential for ongoing collaboration after successful delivery.


Scope of Work
You'll build a standalone Python-based service that:
Accepts short user-uploaded videos
Extracts and processes video frames using FFmpeg/OpenCV
Transcribes audio using Whisper
Analyzes visual and audio content with multimodal LLMs (OpenAI GPT-4o or Gemini)
Generates structured JSON outputs via REST APIs
Delivers clean, production-ready, well-documented code
A complete PRD, API specifications, JSON schemas, and architecture documentation will be provided to shortlisted candidates.

🚀 **Hiring Now – AI Video Data Collection Project (Paid)**

🌍 **Open to:** USA, Mexico, Brazil, Colombia & Argentina

📱 **Eligible Phones:**
• iPhone 12–17 (except 16e & 17e)
• Google Pixel 6–9
• Samsung Galaxy S21–S24

🏠 Work from Home
⏰ Flexible Schedule
💰 Paid Opportunity
🤖 More AI projects available for top performers

**Interested? Send:**
✅ Country
✅ Phone Model
✅ Head Strap (Yes/No)
✅ Availability

📩 Apply now and start earning by contributing to cutting-edge AI & Robotics projects!

Job Overview

We are building an enterprise-grade AI Video Intelligence Platform (VMS) that processes hundreds of live CCTV camera streams in real time. We are looking for an experienced Senior Backend / AI Video Analytics Engineer with strong expertise in video streaming, distributed systems, computer vision, and scalable backend architecture.

If you have hands-on experience building production-grade video analytics platforms, we would love to work with you.

Responsibilities:

*Video Streaming & Processing
1. Design and maintain scalable RTSP video ingestion pipelines using FFmpeg and GStreamer
2. Optimize video streams through FPS throttling, frame sampling, motion filtering, and bandwidth optimization
3. Publish video frames and metadata to Apache Kafka for downstream AI processing
4. Ensure high-performance, low-latency processing across hundreds of simultaneous camera streams


AI Analytics Pipeline
- Develop Kafka consumers for real-time AI inference
- Integrate NVIDIA Triton Inference Server
- Deploy and optimize ONNX deep learning models
- Build face recognition pipelines using:
*SCRFD
*ArcFace
*Qdrant Vector Database
- Optimise inference performance for GPU environments


Live Streaming & Video Storage
1. Implement HLS live streaming and video restreaming
2. Build continuous recording services using MinIO / Amazon S3-compatible storage
3. Develop:
- Video retention policies
- Event clip generation
- Snapshot services
- Video evidence retrieval

Platform & Backend Services
1. Develop multi-tenant configuration management
2. Build WebSocket-based real-time alerting services
3. Implement:
- Detection visualization
- Event exploration
- Face gallery management
- Event history APIs


Infrastructure & DevOps
1. Manage Docker-based deployments
2. Maintain and optimize:
- Apache Kafka
- PostgreSQL
- MinIO
- NVIDIA Triton
3. Monitor application performance, health, and scalability
4. Troubleshoot production issues across streaming, AI inference, and distributed services

Required Skills
- Python (Advanced)
- FastAPI / Flask
- FFmpeg
- GStreamer
- RTSP / RTP / HLS
- Apache Kafka
- Docker
- PostgreSQL
- Redis
- MinIO / Amazon S3
- NVIDIA Triton Inference Server
- ONNX Runtime
- OpenCV
- Computer Vision
- Face Recognition (SCRFD, ArcFace)
- Qdrant Vector Database
- WebSockets
- Linux
- Git

Nice to Have
- CUDA optimization
- TensorRT
- DeepStream SDK
- Kubernetes
- YOLO models
- ByteTrack / DeepSORT
- Multi-camera AI systems
- Video Management Systems (VMS)
- Edge AI deployments

Ideal Candidate
- 5+ years of backend development experience
- 3+ years working with AI video analytics or computer vision
- Experience building large-scale distributed systems
- Strong understanding of event-driven architectures
- Comfortable processing hundreds of concurrent video streams
- Able to write clean, production-ready, scalable code

Project

This is a long-term engagement to build an enterprise AI-powered Video Management System (VMS) for commercial and industrial customers. The right candidate will have the opportunity to become a core technical contributor as the platform scales.

To Apply

Please include:

1. Links to similar AI Video Analytics or VMS projects.
2. Experience with FFmpeg, GStreamer, Kafka, and NVIDIA Triton.
3. Your experience with face recognition pipelines (SCRFD, ArcFace, etc.).
4. Your preferred tech stack and architecture for processing 500+ RTSP camera streams.
5. Your GitHub or portfolio (if available).

Title: AI-Based Low-Cost Video Analytics System for Assisting Grassroots Football Talent Identification

Budget

₹2,500 INR (Fixed Budget)

Project Requirements
Write a complete research paper based on the above topic.
Follow a standard research paper structure:
Abstract
Introduction
Literature Review
Proposed Methodology
Results/Discussion (or Proposed Framework)
Conclusion
Future Scope
References
Format the paper according to IEEE.
Use recent and relevant research papers as references.
Ensure the paper is original, plagiarism-free, and includes proper citations and references.
The writing should be publication-quality, academically sound, and professionally formatted.
Provide the final document in an editable Microsoft Word format (LaTeX is optional).
Preferred Skills
Experience in research paper writing.
Knowledge of Artificial Intelligence, Computer Vision, Machine Learning, or Sports Analytics.
Prior experience with Scopus or Web of Science (WoS) publications is a plus.
Important Notes
Please do not use fabricated or fake references.
All citations must correspond to genuine published research.
Share samples of previous research papers (if available) with your proposal.
Mention your estimated delivery time.

Skills: Research, Technical Writing, Editing, Ghostwriting, Article Writing, Research Writing, Statistical Analysis, Academic Writing, Data Analysis, Computer Vision

Job Summary:

We are seeking an experienced Remote AI Expert to lead the development, implementation, and optimization of artificial intelligence solutions across our organization. The ideal candidate will possess strong technical expertise in machine learning, large language models (LLMs), automation, data analysis, and AI-driven business solutions. This role requires a combination of technical proficiency, strategic thinking, and the ability to translate business needs into scalable AI applications.

Key Responsibilities:

• Design, develop, and deploy AI and machine learning solutions to improve business operations and productivity.
• Implement and optimize Large Language Models (LLMs) such as ChatGPT, Claude, Gemini, and other AI platforms.
• Create and manage AI-powered workflows, automations, and integrations.
• Develop prompt engineering strategies to maximize AI performance and accuracy.
• Analyze business processes and identify opportunities for AI adoption and optimization.
• Collaborate with stakeholders to define AI requirements and project objectives.
• Build and maintain AI agents, chatbots, virtual assistants, and knowledge management systems.
• Monitor AI system performance and continuously improve model outputs and efficiency.
• Ensure compliance with data privacy, security, and ethical AI standards.
• Stay current with emerging AI technologies, tools, and industry trends.
• Perform other duties related to the position as assigned.
Qualifications & Requirements:
• Proficient level of English (written and spoken).
• Experience in Computer Science, Artificial Intelligence, Data Science, Engineering, or a related fields.
• Excellent analytical, problem-solving, and communication skills.
• 3+ years of experience working with AI, machine learning, or data science technologies.
• Strong understanding of generative AI, large language models, and prompt engineering.
• Experience with Python and AI-related frameworks such as TensorFlow, PyTorch, LangChain, LlamaIndex, or similar technologies.
• Experience integrating AI solutions with APIs, databases, and business systems.
• Knowledge of machine learning concepts, model evaluation, and deployment methodologies.
• Excellent analytical, problem-solving, and communication skills.
• Ability to explain complex AI concepts to technical and non-technical audiences.
• Strong work ethic and commitment to meeting deadlines in a fast-paced environment.
• Quick learner with the ability to adapt to new systems, software, and processes.
• Proficiency with Microsoft Office (Word, Excel, Outlook), and standard business tools (email, spreadsheets, document management).
• Out-of-the-box thinker, highly adaptable, reliable, self-motivated, and confident approach.
• Positive attitude and the ability to learn and adapt quickly.
• Ability to understand and follow established processes accurately with minimal supervision.
• Ability to work U.S. Eastern Time (New York) business hours and adapt to business needs.
• Interested in long-term career opportunities.
• Reliable computer (Windows 10 or newer), two monitors, and stable high-speed internet.

Compensation & Benefits:
• 100% remote work.
• Compensation in USD.
• Full-time position with 40 hours weekly.
• Please note that this is a long-term opportunity.
• Great work environment with potential for growth.

We're building a computer-vision pipeline that turns raw soccer match video
into player tracking data, physical/tactical performance metrics, and
opponent scouting profiles, inside a Django-based sports SaaS platform. We
need a computer vision engineer to take this from an early prototype to a
validated, production-quality pipeline, delivered in sequential phases.

CURRENT STATE
Player detection runs on YOLO but without proper filtering/validation;
tracking is a rough prototype with no occlusion handling; team assignment,
real-world coordinate mapping, physical metrics, ball detection, and event
detection are either missing or not reliable.

SCOPE — 6 PHASES (each independently deployable behind a config flag)
- Phase 0: fix detection filtering, real FPS handling, and build an
evaluation harness (annotated reference clips + automated scoring) that
every later phase is measured against.
- Phase 1: integrate a robust multi-object tracker (e.g. ByteTrack) and
assign teams via jersey-color clustering with temporal voting.
- Phase 2: camera-to-pitch coordinate calibration (homography) so positions
can be measured in real meters.
- Phase 3: trajectory smoothing and real distance/speed/sprint/acceleration
metrics with plausibility checks; remove fabricated team metrics, add the
ones genuinely computable (compactness, width, etc.).
- Phase 4: ball detection (small, hard object), possession, passes, duels,
shots. Highest-uncertainty phase — we plan to start with a time-boxed
proof of concept before committing to full scope.
- Phase 5: population-based normalization of player/team profiles.
- Phase 6: production hardening — adaptive performance tuning, GPU
evaluation, QA artifacts visible to end users.

You don't need to commit to all 6 phases up front — happy to start with
Phase 0–1 (or 0–2) as a paid trial engagement and continue from there.

TECH ENVIRONMENT
Python, Django, OpenCV, Ultralytics YOLO, Supervision (ByteTrack), SciPy,
scikit-learn. Heavy CV code runs inside a Celery worker, isolated from the
Django serverless deployment.

WHAT WE'RE LOOKING FOR
- Proven experience with object detection (YOLO or similar) and
multi-object tracking in video.
- Experience with camera calibration / homography for pixel-to-real-world
coordinate mapping.
- Comfortable defining measurable, defensible metrics — validated against
ground truth, not "looks about right."
- Bonus: sports analytics, small-object detection (ball/puck tracking), or
SAHI/tiling techniques.
- Python proficiency; able to work inside an existing Django/Celery codebase
without introducing heavy imports into the serverless layer.

A short technical brief is available attached to this post.

We're looking for a senior Django + DevOps engineer to take a partially-built
video-processing module from demo mode to a production-ready deployment.

PROJECT CONTEXT
Our product is a Django SaaS for sports performance management. One of its
modules processes uploaded match videos through a computer-vision pipeline
(separate work stream, not part of this job) to produce analytics. The module
already has most of its plumbing built — models, API endpoints, async task
structure — but needs consolidation and a production-grade deployment split
across two platforms:

- Web/API layer: Django on Vercel (serverless)
- Heavy processing worker: Celery on Railway (persistent container)
- Message queue: Redis (Upstash)
- Database: PostgreSQL (Neon)
- Video storage: Vercel Blob

WHAT YOU'LL DO
- Finalize Django migrations and audit multi-tenant data isolation (all data
must be scoped per client/organization) across views, permissions and
query selectors.
- Provision and configure the Celery worker + beat scheduler on Railway, and
the Redis broker on Upstash.
- Propagate environment configuration correctly across Vercel and Railway
(they don't share config automatically).
- Harden file upload validation and error handling for the async flow.
- Add baseline logging/observability per processed job.
- Expand automated test coverage for the async task flow.
- Critical constraint: heavy dependencies must never load in the Vercel
serverless runtime — they must stay isolated inside the worker.

WHAT WE'RE LOOKING FOR
- Strong hands-on experience with Django in production, including
Celery-based async processing.
- Experience with serverless deployments (Vercel or similar) AND persistent
container platforms (Railway or similar: Render, Fly.io, Heroku).
- Comfortable working across Postgres, Redis, and cloud object storage.
- Experience auditing multi-tenant SaaS data isolation.
- Bonus: experience with video-processing or ML-inference async pipelines.

DELIVERABLE
A real video upload flows end-to-end through the production worker, correctly
scoped by tenant, without breaking the existing Vercel deployment.

A short technical brief is available attached to this post.

B2B Outbound Lead Gen, Full Funnel, Results Based, Custom CV/ML Offer (USD 1k to 5k deals)

I need 1 to 2 qualified sales calls held per month with prospects who can buy a USD 1k to 5k+ custom computer vision build. Cold email and or LinkedIn. You build and run the whole funnel, I approve the ICP and close the calls. Pay is USD 500 to 800/month and scales with leads delivered.

WHO WE ARE
We are a computer vision and ML studio. We build bespoke systems (sports analytics, retail vision, detection systems), USD 1k to 5k+ per project. Most of our work is inbound today. I am the founder and I want a predictable outbound channel run by someone who owns the number, not someone who sets up a system and disappears. Deal value is high enough that 1 to 2 held calls a month pays off. This is a mid ticket, high margin offer, not a volume game.

WHAT TRIGGERS YOUR PAY (read this carefully)
You are paid when a call is HELD and the prospect met this bar at the time of booking:
1. Works at a company on the approved target list (you build the list, I sign off before sends),
2. Holds a title from this list: founder, co founder, CEO, head of product, head of ops, or technical lead,
3. Replied stating a specific use case or problem,
4. Confirmed a specific time on my calendar.
HELD means they joined the call and stayed at least 10 minutes. Whether they ultimately have budget or buy is my risk, not yours, and does not change your pay. No shows, students, agencies pitching me, and just curious replies do not count.

TARGET AND RAMP (honest)
Month 1 is setup: domain warmup, list build, first sends, no meetings expected. First held calls expected month 2. Realistic target is 1 to 2 held calls a month for the first 2 to 3 months while the fresh niche list runs. After that we review together, broaden the ICP, or add channels. This niche list is finite and I am not pretending it refills forever. The number is a target, not a penalty. You are paid per held call regardless of hitting it. But if held calls stay at zero for two straight months after ramp, I end the contract. A number offered with no caveats is a red flag to me, not a plus. Tell me what it depends on.

PAY STRUCTURE (all through Upwork, nothing off platform)
- Month 1, build and ramp: flat USD 500 fixed milestone for the full build.
- Month 2 onward: base USD 400 + USD 100 per qualified call held. Lands USD 500 to 800/month depending on leads (1 call = 500, 4 = 800).
- Close bonus: USD 150 per closed project from your booked call, on signature and first cleared invoice, within 90 days.
- Tools and data: up to USD 150/month reimbursed on receipts.
Pay scales with delivered held calls. If you want a fixed salary with no result link, this is not the role.

WHAT I PROVIDE
- Offer sheet and 2 case studies with demo clips
- Approval of your proposed ICP before any sends, and fast answers to prospect questions
- Calendly booking link
- Tools and data budget (Instantly or Smartlead, Apollo or equivalent)
- I close the calls

WHAT YOU BUILD AND RUN (full funnel)
- Propose the ICP and target company list (I approve before sends)
- Copywriting, list building, data sourcing
- Sending and deliverability (warmup, sequencing, inbox health)
- CRM and pipeline, set up in an account I own
- Follow up, booking, no show reduction
- Weekly reporting on the numbers below
You build and run it end to end. You own the number. I own the accounts and data.

DOMAIN, IDENTITY, AND BRAND SAFETY (hard rules)
- We hire and pay through Upwork. No payment or contact off platform.
- All sending domains are close variants of our company domain, registered and paid by me, under my accounts. Inboxes, lists, reply data, and the sequencing tool login are mine and stay mine when the contract ends. You operate them, you do not own them.
- You send under your own real name as a member of our outreach team, with our company name, a valid physical address, and a working opt out in every email. This is honest and CAN SPAM clean. Never as me personally, and never as an invented persona with no real inbox behind it.
- EU and UK contacts are handled to GDPR and PECR legitimate interest standards or excluded. You own the compliance of what you send.
- No outreach or scraping on Upwork itself. If LinkedIn is used we agree the method first, no aggressive automation that risks the account.

MUST HAVE
- 3+ year of B2B outbound or appointment setting with real held meeting numbers you can show on a live dashboard
- Sold technical or services offers, not e commerce, not B2C
- Own your cold email and deliverability knowledge
- Comfortable with a mostly performance based structure

HOW TO APPLY
Do not send a generic proposal. Answer these questions in order. Applications that ignore them are declined.

1. Real numbers. Paste your last 90 days of outbound for one campaign: messages sent, reply rate, meetings booked, meetings that actually showed. Add deals closed only if you truly have it, write unknown if you do not. Name the niche and the price point. Do not inflate, I will ask you to screen share the dashboard.
2. How many held qualified calls a month will you commit to for a USD 1k to 5k custom CV offer once ramped, and what three things would push that number lower?
3. In one line, what counts as a qualified meeting to you, and what do you refuse to count?
4. If I give you only my offer and a rough ICP direction, walk me through month 1: what you build (ICP, list, copy, domains, CRM, sequences), in what order, and what exactly you need from me.
5. Write the actual 3 line cold email you would send to the founder of a sports tech startup to book a call about grappling or racket sport video analytics.
6. A prospect replies "interesting but no budget right now". What do you send back?
7. What do you report to me each week, in what format, and which single number do you hold yourself to?
8. Share a screen recording or redacted screenshot of your sending tool dashboard (Instantly or Smartlead) for one real campaign showing sends, replies, and booked meetings. A reference is optional and secondary.
9. How do you keep cold sends CAN SPAM and GDPR or PECR compliant?

We are looking for a senior NVIDIA Physical AI Engineer or Solution Architect to support the development of industrial AI solution offerings based on the NVIDIA technology ecosystem.

The initial engagement will focus on assessing a broad set of potential industrial use cases and determining which NVIDIA technologies, machine-learning methods and solution architectures are most appropriate. The work will cover areas such as industrial automation, robotics, digital twins, simulation, synthetic data generation and AI-assisted engineering.

This is primarily a technical engineering and solution-design role. Other team members will cover project management, commercial strategy and customer-facing consulting.

Responsibilities

- Assess industrial use cases against the capabilities of the NVIDIA technology ecosystem.
- Evaluate relevant technologies, including Omniverse, OpenUSD, Isaac Sim, Isaac Lab and Cosmos.
- Select appropriate NVIDIA components and ML approaches for each use case.
- Define high-level solution and reference architectures.
- Identify data, integration, infrastructure and GPU workload requirements.
- Assess technical feasibility, dependencies, risks and implementation complexity.
- Help prioritise the strongest use cases for future proofs of concept.
- Define the technical scope and roadmap for subsequent PoC and implementation phases.
- Provide technically credible input for customer and management presentations.

The initial phase will not include building a working prototype. However, we are looking for someone with the technical capability to support or implement subsequent PoCs.

Required experience

- Strong hands-on experience with NVIDIA technologies, particularly Isaac Sim, Isaac Lab, Omniverse and Cosmos.
- Practical experience with robotics simulation, physical AI, synthetic data, digital twins or sim-to-real workflows.
- Strong machine-learning knowledge relevant to robotics, computer vision, reinforcement learning or industrial AI.
- Experience with Python and ML frameworks such as PyTorch.
- Experience designing solutions for industrial automation, manufacturing, automotive, energy, logistics or engineering environments.
- Ability to translate an initial use case into a technically feasible architecture and implementation plan.
- Previous involvement in real industrial, applied-research or customer implementation projects.
- Experience with ROS 2, Isaac ROS, Replicator, GR00T, NVIDIA NIM, OpenUSD, CUDA or industrial engineering systems would be valuable.

This is not a general AI, cloud infrastructure, project-management or transformation role. The core requirement is deep NVIDIA and ML expertise combined with the ability to design technically feasible industrial solutions.

The engagement is expected to run for approximately four to six weeks at roughly two to three days per week. Successful work may lead to subsequent proof-of-concept and implementation projects.

Please begin your proposal by naming the two NVIDIA technologies in which you have the strongest hands-on experience. Include one relevant industrial or applied-research project and clearly describe your personal technical contribution. Generic AI or cloud architecture proposals will not be considered.

Hello,

We are looking for an experienced freelance AI / computer vision engineer to advise on and develop a local AI environment for thermal-camera pattern recognition.

The work would involve:

Defining the hardware, software and data requirements
Setting up footage collection and storage
Processing thermal-camera data in a meaningful way
Identifying suitable AI or computer-vision methods
Developing an initial proof of concept
Integrating the solution with existing software

We are looking for someone with experience delivering similar camera-analysis projects who can ideally demonstrate a relevant previous system or prototype.

Some on-site work in Bedford will be required, with the remaining work completed remotely.

We are looking to develop a computer vision solution for automated analysis of product and retail images.

The system should be able to process images captured in real-world environments, identify relevant products or objects, and organize the extracted visual information into a structured and usable format.

Project objectives:

Detect and recognize products or selected object categories within images
Handle different camera angles, lighting conditions, image quality, and partial occlusion
Extract useful visual data such as object location, quantity, category, or condition
Support processing of multiple images and larger image datasets
Provide results through a simple interface, dashboard, or API
Build a foundation that can later be expanded with additional recognition and analytics features
We are looking for an end-to-end project implementation, including solution architecture, model development or integration, backend processing, testing, and deployment.

The initial phase may include a prototype or proof of concept, followed by further development based on the achieved results.

Relevant experience with computer vision, object detection, image processing, and production-ready ML systems would be valuable.

We are looking to develop a computer vision solution for automated analysis of product and retail images.

The system should be able to process images captured in real-world environments, identify relevant products or objects, and organize the extracted visual information into a structured and usable format.

Project objectives:

Detect and recognize products or selected object categories within images
Handle different camera angles, lighting conditions, image quality, and partial occlusion
Extract useful visual data such as object location, quantity, category, or condition
Support processing of multiple images and larger image datasets
Provide results through a simple interface, dashboard, or API
Build a foundation that can later be expanded with additional recognition and analytics features
We are looking for an end-to-end project implementation, including solution architecture, model development or integration, backend processing, testing, and deployment.

The initial phase may include a prototype or proof of concept, followed by further development based on the achieved results.

Relevant experience with computer vision, object detection, image processing, and production-ready ML systems would be valuable.

Face Image Collection Project – Simple Remote Task (9–24 Photos)

We are looking for participants to contribute to a face image collection project that supports AI research and the improvement of facial recognition technology.

This is a simple, remote, one-time task that can be completed using your smartphone.

Project Purpose

The images collected will be used solely for research and training computer vision models to improve face recognition technology. Your photos will not be publicly shared, sold, or used for advertising purposes.

What You'll Submit

You will upload 9–24 images in a single submission, including:

* 2–4 recent selfies taken at the time of submission.
* 2–4 new selfies with specific head poses (instructions will be provided inside the task).
* 5–16 older photos of yourself taken over the past 10 years.

Photo Requirements

To ensure your submission is accepted:

* Your full face must be clearly visible.
* Eyes must be open and visible.
* No sunglasses, masks, hats, or anything covering your face.
* No hair, hands, or objects blocking your face.
* No mirror selfies.
* No filters, beauty effects, or edited images.
* Photos must be clear, well-lit, and in focus.
* Only you should appear in the photos.
* Please avoid submitting multiple nearly identical photos.

Eligibility

* You must be at least 18 years old.
* You must be able to provide both recent and historical photos of yourself.
* You must follow the task instructions carefully.
* Applicants must be located in an eligible country or region. Due to project restrictions, applications from certain countries and regions cannot be accepted.

Payment

Payment will be made after your submission has been reviewed and approved for meeting the project requirements.

How to Apply

Please submit your proposal with:

* Your current country of residence.
* Confirmation that you can provide 9–24 qualifying images.
* Confirmation that you have read and understood the project requirements.

Selected applicants will receive detailed instructions and secure access to the image submission platform.

We look forward to working with you!

We are seeking an experienced machine learning researcher to provide on-call consulting support during a revision process for a computer vision/medical AI paper.

The paper investigates the impact of synthetic image replacement on external generalization in dermatology image classification. We received reviewer feedback requiring assessment of experimental methodology, ablation design, synthetic data quality control, and academic presentation.


We need a consultant who can provide strategic guidance on:

Evaluating reviewer criticisms and determining whether they require:
- additional experiments,
- manuscript clarification,
- limitation acknowledgment,
- or rebuttal-only responses.

Reviewing our response-to-reviewers strategy.
Identifying weaknesses that may threaten acceptance.
Suggesting additional experiments or analyses with high reviewer impact-to-effort ratio.
Improving scientific framing and avoiding ineffective rebuttals.
Evaluating technical responses to reviewers when needed.


Preferred Background
The ideal consultant has:
PhD or equivalent research experience in:
- machine learning,
- computer vision,
- medical imaging AI,
- generative AI,
- other related fields.

Experience publishing in venues such as:
IEEE conferences/journals,
CVPR,
MICCAI,
NeurIPS,
AAAI

Experience responding to peer review comments or peer reviewing in IEEE venues.

Strong understanding of:
- ablation studies,
- experimental validity,
- synthetic data evaluation,
- model generalization,
- reviewer expectations.


Current Needs
Initial tasks:

Review reviewer comments and our draft responses.
Provide an acceptance-risk assessment.
Recommend which issues require:
- new experiments,
- revised wording,
- additional limitations,
- or rebuttal arguments.
Help refine responses to maximize reviewer confidence.

Potential ongoing support:
- Additional reviewer response drafting.
- Manuscript editing.
- Experimental design advice.
- Final submission review.


Deliverables
- Written assessment of reviewer concerns.
- Recommended revision strategy.
- Evaluated reviewer responses when requested.
- Clear prioritization of required versus optional changes.
- Engagement
- On-call availability during revision period preferred.


When applying, provide:
- Relevant publications.
- Prior experience with ML peer review/revisions.
- Examples of papers you helped revise (if available).

I need a lightweight, camera-agnostic application that taps into the IP feeds already installed at my parking lot and shows, live, how many cars and motorcycles have entered and exited. Accuracy and low latency are critical because the numbers will feed straight into our occupancy display as well as daily summaries we archive.

Here’s what I’m expecting:

• Software (GUI or web dashboard) that connects to multiple RTSP or ONVIF streams and automatically detects entry and exit lines.
• Separate, real-time tallies for cars and motorcycles, with the running balance always visible.
• A simple way to reset or export counts (CSV/JSON) at the end of a chosen period.
• Installation guide plus brief documentation so my team can add new cameras later.

OpenCV, YOLOv8, TensorFlow, or a comparable computer-vision stack is fine as long as the final solution runs reliably on a mid-range Windows or Linux box without expensive GPUs.

I’ll consider the project complete when I can point the finished program at two of our existing cameras, watch vehicles come and go, and see the live numbers update with at least 95 % accuracy verified over a one-hour test.

Skills: PHP, Python, Software Architecture, C++ Programming, OpenCV, Computer Vision, AI Model Development, AI Integration

PROJECT OVERVIEW

We are looking for an experienced QA engineer to evaluate a document extraction and OCR SDK for Japanese-language documents.

Budget: USD 1,500 fixed price
Duration: Approximately 10 business days of active work
Target completion: August 31, 2026
Start: As soon as possible

The SDK runs on Linux x64. The selected contractor will install the SDK, test it against Japanese invoices, contracts, forms, and scanned documents, measure extraction accuracy, document reproducible defects, and develop an automated regression test program.

This project is focused on measuring and documenting the SDK's current quality. Improving the OCR engine to reach a specific accuracy target or making major changes to the SDK itself is not part of the scope.

The SDK, license, English documentation, and available test materials will be provided after contractor selection and, if required, execution of an NDA.

SCOPE LIMITS

- Up to 50 document files, with a maximum of 150 pages in total
- One agreed Linux x64 environment
- Up to 20 Japanese UI screens and 30 pages of documentation for language review
- One initial test cycle and one corrected-SDK retest
- One agreed set of extraction fields
- Waiting time for a corrected SDK is not included in the 10 active business days

SCOPE OF WORK

1. Environment setup

- Prepare or use a Linux x64 test environment
- Install the SDK and required dependencies
- Configure licensing or authentication
- Confirm supported input and output formats
- Run an initial smoke test
- Review SDK errors and logs

2. Test planning

- Classify document types and extraction fields
- Define test cases and expected results
- Define the ground-truth data format
- Define accuracy metrics and defect severity levels
- Confirm acceptance criteria before full testing

3. Test data and ground truth

- Organize up to 50 approved documents / 150 pages total
- Create or normalize expected results in JSON or CSV
- Map each test document to its expected output
- Ensure that personal and confidential information is handled appropriately

Expected document types may include:

- Japanese invoices, contracts, application forms, and business forms
- Scanned, skewed, noisy, or low-resolution documents
- Documents containing vertical Japanese text
- Mixed kanji, hiragana, katakana, Latin characters, and numbers
- Tables and line items
- Stamps or seals
- Dates, amounts, currencies, names, companies, and addresses
- Multi-column or otherwise complex reading order

Test data may consist of approved public data, synthetic data, or data supplied securely by the client.

4. OCR and extraction testing

Evaluate items such as:

- Company and personal names
- Addresses, telephone numbers, and email addresses
- Invoice, document, and contract numbers
- Issue dates and payment due dates
- Subtotals, tax, totals, and currencies
- Product or service names, quantities, and unit prices
- Line items and table structures
- Stamps or seals, where supported
- Text reading order

Also record crashes, timeouts, encoding problems, and unexpected errors.

5. Accuracy evaluation

Compare SDK output with ground truth and classify results as:

- Exact match
- Partial match
- Missing extraction
- Incorrect extraction
- Incorrect field assignment
- Character corruption
- Broken table structure
- Incorrect reading order

Where appropriate, calculate field-level accuracy, document-level accuracy, precision, recall, F1 score, and character or word error rate.

Reaching a specific accuracy level is not an acceptance requirement.

6. Defect investigation

Each reported problem should include:

- Issue summary and severity
- Affected document and input conditions
- Expected and actual results
- Reproduction steps
- Relevant SDK output and logs
- Screenshot or other supporting evidence
- Reproduction frequency
- Suggested workaround or improvement, where possible

7. Automated regression test program

Develop a reproducible test program, preferably in Python, that can:

- Process all documents in a specified directory
- Execute the SDK automatically
- Save raw SDK output
- Compare output with JSON or CSV ground truth
- Detect differences and determine pass/fail status
- Aggregate results by document and field
- Export results in CSV and/or JSON
- Save execution logs
- Compare the current run with a previous run

Another language may be used if required by the SDK interface.

8. Japanese UI and documentation review

Review the available Japanese UI or Japanese-facing materials for:

- Unnatural Japanese
- Translation errors or inconsistent terminology
- Buttons and error messages
- Display problems in a Japanese environment
- Missing or unclear setup instructions
- Areas likely to confuse Japanese users

If editable source files are unavailable, provide proposed corrections in the final report.

9. One regression retest

If a corrected SDK is supplied within the agreed schedule, perform one retest to confirm:

- Previously reported problems have been addressed
- Existing document processing still works
- Accuracy has not materially regressed
- No new crashes or major errors have appeared

DELIVERABLES

- Test plan and test case list
- Approved test data and structured ground-truth data
- Regression test source code
- Environment setup and execution instructions
- Document-level and field-level accuracy results
- Defect list
- Reproduction steps, logs, screenshots, and supporting evidence
- Japanese UI and documentation improvement proposals
- Results of one corrected-SDK retest, if the SDK is supplied on schedule
- Final report and handover materials

Any restrictions on redistributing third-party or confidential test data must be documented.

ACCEPTANCE CRITERIA

The project will be accepted when:

- The SDK can be executed in the agreed Linux x64 environment
- Up to 50 agreed documents / 150 pages have been tested
- Results are recorded for the agreed extraction fields
- Reported defects contain evidence and reproduction instructions
- The regression program can be rerun using the provided instructions
- Source code and accuracy reports have been delivered
- Japanese UI and documentation issues have been documented
- One retest has been completed if the corrected SDK is provided within the agreed schedule
- Final reporting and handover are complete

Acceptance does not require the SDK to reach a specific accuracy level or for every reported defect to be fixed.

OUT OF SCOPE

- Major SDK source-code modifications
- Development of a new OCR engine
- AI model training or fine-tuning
- Production integration
- Building or operating commercial infrastructure
- Testing beyond the agreed document/page limit
- More than one corrected-SDK retest
- Extensive UI or documentation rewriting outside the agreed limits
- Ongoing production-data processing
- Guaranteeing OCR accuracy

Additional work will require a separate estimate and milestone.

SECURITY REQUIREMENTS

- Do not upload documents to external services without written approval
- Do not reuse test data for another purpose
- Protect personal and confidential information
- Sign an NDA if required
- Follow the agreed deletion procedure after completion
- Do not disclose the SDK, source code, or test results to third parties

REQUIRED QUALIFICATIONS

- Professional or native-level Japanese
- Experience testing OCR, document AI, or document extraction systems
- Strong Python test-automation experience
- Linux x64 development and troubleshooting skills
- Experience working with JSON, CSV, APIs, logs, and command-line tools
- Ability to build structured ground-truth datasets
- Understanding of precision, recall, F1, and OCR error metrics
- Clear written reporting in English
- Ability to handle confidential materials securely

Experience with Japanese invoices, contracts, table extraction, reading-order evaluation, or image preprocessing is a plus.

PROPOSED MILESTONES

1. SDK setup, smoke test, test plan, and test-case design: USD 250
2. Ground truth, full testing, accuracy evaluation, defect evidence, and regression program: USD 950
3. One corrected-SDK retest, final report, and handover: USD 300

Total: USD 1,500

PLEASE INCLUDE IN YOUR PROPOSAL

1. Whether you can complete the project
2. Your earliest available start date
3. Whether you can complete approximately 10 business days of active work by August 31, 2026
4. Number of team members and their roles
5. Relevant OCR, document AI, Japanese-language testing, and automation experience
6. Estimated effort for each project phase
7. Proposed technologies and test environment
8. What you require from us before starting
9. Confirmation that you accept the USD 1,500 fixed budget
10. Assumptions, possible additional costs, and current questions

Please briefly describe one relevant OCR or document-processing project and your specific contribution.

I need you to lecture and train me in ai platforms...

1. Claude.

2. Chatgpt.

3. Gemini

4. Manus.

5. Perplexity

I need to learn how to use " AGENTS "

Within each platform

And I need to build my websites using ai

So I am looking for a genuine teacher from any of these platforms


Please outline your expertise in either of these platforms in ur reply

Do not waste your time.

Be PRECISE OK?





Scope
• Cover the full spectrum of modern AI: classic machine-learning workflows, natural-language processing, computer vision, and emerging multimodal techniques.
• Wherever a concrete example fits—image classification, text generation, or small end-to-end projects—walk through the code step by step. Feel free to showcase TensorFlow, PyTorch, Keras, or any other mainstream library if it helps keep explanations clear and practical.
• Position every lesson for self-paced study: concise theory, a runnable notebook or script, annotated output, plus brief “Why this matters” commentary to keep motivation high.

Deliverables
1. Structured tutorial content (written or slide deck) paired with executable code samples.
2. Well-commented notebooks or scripts that run on a standard laptop or free Colab instance.
3. A short recap or Q&A section at the end of each topic so learners can check their understanding.

Acceptance Criteria
• A complete beginner can reproduce results without extra setup beyond the instructions you supply.
• Code is tidy, properly documented, and references package versions used.
• Explanations remain accessible: whenever you introduce a new term, define it in plain language first.

If this sounds like your sweet spot in teaching and coding, let me know how you would structure the series and share a sample of your previous educational work.

Skills: PHP, Website Design, Graphic Design, HTML

I revised the description you posted to preserve the core platform, security, permanent-redaction, ownership, and discovery requirements while making it more suitable for a worldwide Upwork posting.

This version reflects a global talent search , a fixed-price discovery phase , separate milestones, and a detailed requirements package shared only with shortlisted applicants. Upwork permits global job posts, fixed-price milestones, and separate NDAs; pre-contract communication should remain on Upwork.

# Senior Full-Stack Developer for Secure AI-Assisted Document Redaction Platform

## Project Overview

We are seeking an experienced senior full-stack developer, technical lead, or small coordinated development team to help design and eventually build a secure, AI-assisted document redaction and records-processing platform.

Applicants may be located anywhere. However, all proposed team members, developers, specialists, and subcontractors must be identified before receiving access to the project.

This is not a request for:

* A basic website
* A general chatbot
* A simple PDF editor
* A basic file-storage application
* A public AI wrapper
* An automated redaction tool without human review
* An application that only places black boxes over visible text

The platform will support a managed document-processing operation that receives electronic archives, scanned records, historical documents, and document backlogs from government agencies, regulated organizations, legal offices, businesses, and other clients.

Authorized personnel will use the platform to:

* Receive client documents securely
* Organize records by client, project, batch, file, and page
* Perform OCR and document-image processing
* Classify documents and pages
* Detect potentially protected or confidential information
* Present suggested redactions to trained human reviewers
* Require human validation of redactions
* Conduct secondary quality-control review
* Permanently redact and sanitize approved documents
* Verify that removed information cannot be recovered
* Generate reports, manifests, metadata, and delivery packages
* Return completed files securely
* Track retention and secure deletion
* Produce audit reports and deletion certificates

Accuracy, confidentiality, security, human validation, secondary quality control, permanent redaction, records integrity, and traceability are essential requirements.

## Location and Communication Requirements

This is a worldwide opportunity.

Applicants must:

* Identify the country and time zone of every person who will work on the project
* Disclose whether the work will be performed by an individual, agency, employees, partners, or subcontractors
* Provide at least two hours of communication overlap with Pacific Time
* Communicate clearly in English
* Attend scheduled video meetings when required
* Provide regular written progress updates
* Obtain written approval before adding or replacing team members

Development may be performed from any approved location. However, live client records and production document processing will remain in a company-controlled environment located in the United States.

## Initial Contract: Paid Discovery Phase

The first contract will be a fixed-price discovery, requirements, architecture, and technical-planning engagement.

The selected developer will not immediately build the complete production platform.

The discovery phase will determine:

* Functional requirements
* Security requirements
* User roles and permissions
* Operational workflows
* Recommended system architecture
* Recommended technology stack
* Database and file-storage design
* OCR and image-processing approach
* AI-assisted detection approach
* Human-review workflow
* Secondary quality-control workflow
* Permanent-redaction methodology
* Document-sanitization methodology
* Audit-logging requirements
* Reporting requirements
* Development and production separation
* Hosting and deployment architecture
* Third-party tools and services
* Licensing and recurring costs
* Technical risks
* Security risks
* Proof-of-concept scope
* Minimum viable product scope
* Development milestones
* Estimated schedule
* Estimated development and operating costs

After successful completion of discovery, the selected developer may be considered for additional milestones involving a technical proof of concept, minimum viable product, security testing, deployment, documentation, maintenance, and support.

## Hosting and Deployment

The company does not currently plan to purchase physical server equipment.

During discovery, the selected developer must recommend an appropriate company-controlled cloud, private-cloud, dedicated-hosting, or hybrid architecture based on:

* Document volume
* Page volume
* File sizes
* OCR requirements
* AI-processing requirements
* Storage requirements
* Security requirements
* Backup and disaster-recovery requirements
* Client requirements
* Expected future growth
* Estimated operating costs

Development should initially use a company-controlled cloud environment with separate development, testing, staging, and production configurations.

All hosting accounts, domains, databases, storage resources, administrator accounts, credentials, encryption keys, source-code repositories, and production configurations must remain under company control.

## Required Platform Capabilities

### Secure Document Intake

The platform should support:

* Secure client and employee accounts
* Individual and bulk document uploads
* Large files and large document batches
* SFTP or another secure transfer method
* Upload progress and status reporting
* File-format validation
* Malware and virus scanning
* Duplicate-file detection
* File-integrity verification
* Intake manifests
* Project and batch identification
* File and page inventories
* Chain-of-custody tracking
* Failed-upload reporting
* Exception reporting
* Configurable retention requirements

### Supported File Formats

The system should support or be designed to support:

* PDF
* Searchable PDF
* PDF/A
* TIFF
* JPEG
* PNG
* Microsoft Word files
* OCR text
* CSV
* XML
* JSON
* Metadata files
* Client-specific index and import files

The architecture must allow additional document and output formats to be added later.

### OCR and Document-Image Processing

Required or anticipated functions include:

* OCR for scanned and image-based records
* Preservation of page, line, word, and coordinate information
* Page-orientation detection
* Rotation and deskewing
* Noise removal
* Image enhancement
* Blank-page detection
* Searchable-text creation
* OCR confidence scoring
* Identification of unreadable or low-confidence pages
* Manual OCR correction
* Document classification
* Page classification
* Document separation
* Document assembly
* Batch processing
* Reprocessing of failed or rejected files
* Processing-status tracking

Applicants should explain whether they recommend established OCR products, open-source OCR tools, private services, locally hosted technology, custom models, or a combination.

### AI-Assisted Protected-Information Detection

The platform should assist trained reviewers with identifying protected, confidential, personal, or client-defined information, including:

* Social Security numbers
* Tax-identification numbers
* Dates of birth
* Driver’s license numbers
* State-identification numbers
* Passport numbers
* Bank-account numbers
* Credit-card information
* Medical or health information
* Signatures
* Email addresses
* Telephone numbers
* Home addresses
* Names of protected individuals
* Information concerning minors
* Legal case information
* Property-record information
* Client-defined names, words, phrases, patterns, fields, or page areas

Detection methods may include:

* Regular expressions
* Pattern matching
* Named-entity recognition
* OCR coordinates
* Document classification
* Machine-learning models
* Private AI services
* Locally hosted models
* Client-specific rules
* Manual reviewer selections

AI findings will be recommendations only. The system must not independently approve or finalize redactions.

### Configurable Client and Project Rules

Administrators should be able to configure separate requirements based on:

* Client
* Agency
* Project
* Jurisdiction
* Document type
* Record series
* Confidentiality category
* Redaction category
* Required output format
* Quality-control level
* Retention period
* Delivery requirements

The system should record which rule set and rule version were applied to each file.

## Human Review and Quality Control

The platform must include a complete human-in-the-loop review process.

Primary reviewers must be able to:

* View the original document
* View OCR text
* Review AI-suggested redactions
* Accept a suggested redaction
* Reject a suggested redaction
* Correct a suggested redaction
* Resize or reposition a redaction area
* Add a missed redaction
* Assign a redaction reason or category
* Add reviewer notes
* Flag uncertain information
* Escalate a document
* Submit completed work for quality control

Secondary quality-control reviewers must be able to:

* Review the original file
* Review proposed and approved redactions
* Examine primary-review decisions
* Approve completed work
* Reject completed work
* Return work for correction
* Add quality-control findings
* Escalate unresolved issues
* Provide final approval

Every action must be associated with the responsible user, date, time, project, batch, file, page, action, and result.

The platform should also support:

* Reviewer assignments
* Supervisor review
* Rework queues
* Exception queues
* Random quality-control sampling
* Full quality-control review when required
* Error categories
* Corrective-action tracking
* Reviewer accuracy reports
* Productivity reports
* Quality trends
* Final completion approval

## Permanent Redaction and Document Sanitization

The completed system must do more than place a visible rectangle or black box over information.

Final processing must permanently remove or sanitize protected information from:

* Visible page content
* Underlying text
* OCR text layers
* Hidden objects
* Hidden layers
* Comments
* Annotations
* Form fields
* Embedded attachments
* Scripts and active content
* Revision information
* Document properties
* Unapproved metadata
* Thumbnail images
* Temporary working files
* Intermediate processing files
* Other recoverable content

The system should verify that protected information cannot be recovered by:

* Copying and pasting
* Selecting hidden text
* Searching the completed file
* Removing a visual overlay
* Extracting the OCR text layer
* Inspecting annotations
* Opening embedded files
* Reviewing metadata
* Examining temporary or intermediate outputs

The system must record redaction and sanitization validation results in the document’s audit history.

## Security and Data Boundary

This engagement concerns software design and development. It does not include outsourced review or processing of live client records.

The following requirements are mandatory:

* Development and testing must use synthetic, simulated, or properly de-identified documents.
* Developers will not have routine or unrestricted access to live client records.
* Live records will remain in a company-controlled production environment.
* Production document processing will occur in the United States.
* Production databases, credentials, administrator accounts, encryption keys, and security configurations will remain under company control.
* Development, testing, staging, and production environments must be separated.
* Production records may not be copied into development or testing.
* Client files may not be retained or used for demonstrations.
* Project information may not be submitted to public consumer AI tools.
* Client records may not be used to train public, private, commercial, personal, or developer-owned AI models.
* Client information may not be retained by third-party services without prior written approval.
* No work may be subcontracted without written approval.
* No unidentified person may access the project.
* Any exceptional production access must be approved, limited, time-restricted, monitored, logged, and capable of immediate termination.

Applicants must identify every proposed external:

* OCR service
* AI service or model
* PDF-processing service
* Storage provider
* Hosting provider
* Logging or monitoring service
* File-transfer service
* Security service
* Third-party library
* Licensed software product

The applicant must disclose the provider’s purpose, data flow, retention practices, licensing terms, and recurring costs.

## Application Security Requirements

Required or anticipated controls include:

* Role-based access
* Least-privilege permissions
* Multifactor authentication
* Secure password controls
* Encryption in transit
* Encryption at rest
* Secure secrets management
* Secure encryption-key management
* Session timeout controls
* Account lockout protections
* User activation and deactivation
* Project-level access restrictions
* Separation of client projects
* Download restrictions
* Access expiration
* Administrative approval
* Detailed audit logs
* Security-event logging
* Secure APIs
* Input validation
* File-integrity controls
* Malware scanning
* Backup and recovery
* Retention controls
* Secure deletion
* Dependency scanning
* Vulnerability scanning
* Automated testing
* Secure error handling
* Production logging and monitoring

Development should use recognized secure software-development practices capable of aligning with the NIST Secure Software Development Framework and using the OWASP Application Security Verification Standard as a basis for web-application security verification. ([NIST Computer Security Resource Center][2])

## Audit Logging

The platform should maintain detailed, meaningful, and tamper-resistant records of activities such as:

* Login attempts
* Authentication failures
* Account changes
* Permission changes
* Document uploads
* File validation
* Document viewing
* Reviewer assignments
* Redaction decisions
* Quality-control decisions
* Administrative actions
* File exports
* Downloads
* Deliveries
* Retention changes
* Deletion events
* Security alerts
* System errors

Audit events should identify the user, date, time, affected client, project, batch, document, page, action, and outcome.

## Required Output Capabilities

Depending on client requirements, the platform should be capable of producing:

* Permanently redacted TIFF files
* Searchable redacted PDFs
* PDF/A files
* OCR text files
* Metadata files
* CSV index files
* XML index files
* JSON index files
* Client-specific import files
* Batch manifests
* File inventories
* Exception reports
* Redaction reports
* Quality-control reports
* Audit reports
* Processing statistics
* Chain-of-custody records
* Secure-delivery confirmations
* Retention reports
* Deletion certificates

## Reporting and Dashboards

The system should provide reports and dashboards showing:

* Files and pages received
* Files and pages processed
* Processing status
* OCR completion
* OCR confidence
* Documents awaiting review
* Redactions proposed
* Redactions accepted
* Redactions rejected
* Redactions corrected
* Redactions manually added
* Reviewer assignments
* Reviewer productivity
* Quality-control findings
* Rework requirements
* Error rates
* Exception rates
* Project completion percentage
* Delivery status
* Retention status
* Deletion status
* Estimated processing charges
* Actual processing charges

## Future Integrations

The architecture should permit future integration with:

* Document-management systems
* Records-management systems
* Government records systems
* Archival systems
* Secure SFTP servers
* Identity-management systems
* Cloud-storage environments
* Billing and accounting systems
* Client databases
* Records indexes
* APIs
* Secure web services

The initial version does not need every future integration, but the architecture must support expansion.

## Preferred Experience

Applicants should demonstrate relevant experience with several of the following:

* Full-stack application development
* Secure web-application development
* Python
* FastAPI or Django
* React or Next.js
* TypeScript
* PostgreSQL
* Redis
* Background-processing queues
* OCR
* Computer vision
* OpenCV
* PDF processing
* TIFF processing
* Document classification
* Named-entity recognition
* Private AI models
* Locally hosted AI models
* Secure API development
* Role-based authorization
* Multifactor authentication
* Audit logging
* Secure file transfer
* Docker
* Cloud deployment
* On-premises deployment
* Automated testing
* Vulnerability testing
* Technical documentation

General website, chatbot, or basic AI-wrapper experience alone is insufficient.

## Discovery-Phase Deliverables

The first paid engagement should produce:

1. Functional-requirements specification
2. Security-requirements specification
3. User-role and permission matrix
4. Operational workflow diagrams
5. System-architecture diagram
6. Data-flow diagram
7. Preliminary database design
8. File-storage and processing design
9. OCR and document-processing recommendation
10. AI and protected-information detection recommendation
11. Permanent-redaction and sanitization design
12. Human-review and quality-control design
13. Audit-logging design
14. Environment-separation design
15. Hosting and deployment recommendation
16. Third-party technology list
17. Licensing and recurring-cost schedule
18. Technical-risk assessment
19. Security-risk assessment
20. Defined technical proof-of-concept scope
21. Defined minimum viable product scope
22. Milestone-based implementation plan
23. Estimated development schedule
24. Estimated proof-of-concept and MVP costs
25. Estimated ongoing hosting, maintenance, licensing, and support costs

## Potential Technical Proof of Concept

Using synthetic documents, a later proof-of-concept milestone should demonstrate:

* Secure document upload
* OCR processing
* Preservation of text coordinates
* Identification of selected protected information
* Human review of suggested redactions
* Acceptance of a suggested redaction
* Rejection of a suggested redaction
* Correction of a suggested redaction
* Manual addition of a missed redaction
* Secondary quality-control review
* Permanent redaction
* Metadata sanitization
* Redaction validation
* Audit logging
* Completed-file export

## Source-Code Ownership and Documentation

The following requirements are mandatory:

* The company must own all paid custom work product.
* Source code must be stored in a private company-controlled repository.
* Work must be committed regularly during development.
* Completed source code may not be withheld until the end of the engagement.
* Code must be readable, organized, tested, and maintainable.
* Credentials and encryption keys may not be embedded in source code.
* All open-source and third-party components must be disclosed.
* All licensing, hosting, API, AI, OCR, maintenance, and recurring costs must be disclosed.
* The developer may not reuse or resell confidential project materials.
* The project may not be published in a portfolio without written approval.
* Work may not be subcontracted without written approval.
* Architecture, database, APIs, installation, configuration, deployment, security, administration, and maintenance procedures must be documented.
* The completed system must be transferable to another qualified developer without requiring a complete rebuild.

The selected applicant may be required to sign:

* A nondisclosure agreement
* A development or independent-contractor agreement
* An intellectual-property and work-product assignment
* A data-security and access agreement
* A subcontractor disclosure

## Proposal Instructions

Begin your proposal with:

**SECURE DOCUMENT PLATFORM**

Then address the following:

1. Identify your location, time zone, and available Pacific Time overlap.
2. State whether you personally perform the work.
3. Identify every person who would have access to the project.
4. Disclose all employees, partners, agencies, or subcontractors who may participate.
5. Describe your experience with OCR and scanned-document processing.
6. Describe your experience with PDF and TIFF processing.
7. Explain your experience with permanent redaction and metadata sanitization.
8. Describe a human-review or quality-control workflow you developed.
9. Explain how you would separate development, testing, staging, and production.
10. Explain how you would prevent unauthorized access to live production records.
11. Provide two relevant project examples and explain which portions you personally completed.
12. Identify your preliminary recommended technology stack.
13. Identify any OCR, AI, PDF, storage, hosting, or security products you would consider.
14. Confirm that source code can remain in a private repository controlled by the company.
15. Confirm that project information and records will not be used for AI training.
16. Confirm that you will not subcontract the work without written approval.
17. Provide a fixed-price estimate and timeline for the discovery phase.
18. Provide a preliminary cost range for the technical proof of concept.
19. State your weekly availability.
20. Describe your proposed milestone and payment structure.

Please provide specific responses. Generic proposals will not be considered.

## Contract Structure

The initial contract will be a fixed-price discovery phase with defined deliverables, deadlines, acceptance criteria, and milestone payments.

Potential additional milestones may include:

1. Technical proof of concept
2. Core platform development
3. Human-review and quality-control functions
4. Reporting and administration
5. Security testing
6. Production preparation
7. Deployment and documentation
8. Maintenance and support

Fixed-price milestones allow the project to be divided into defined portions of work with separate deliverables and payments. ([Upwork Support][3])

## Selection and Next Steps

Shortlisted applicants may be invited to:

* Participate in an Upwork video interview
* Explain a relevant OCR, document-processing, secure SaaS, or redaction project
* Complete a small paid technical evaluation
* Review and sign required agreements
* Review the complete project-requirements package
* Submit a final fixed-price discovery proposal

A detailed project-requirements package will be provided through Upwork to shortlisted applicants. An NDA may be required before nonpublic business, workflow, architecture, or security information is shared.

Cost is important, but the lowest proposal will not automatically be selected. Selection will consider:

* Relevant document-processing experience
* Permanent-redaction knowledge
* Security awareness
* Full-stack technical ability
* Communication
* Documentation
* Reliability
* Cost
* Availability
* Ability to preserve the platform’s purpose and requirements

All pre-contract communication, interviews, file sharing, and negotiations must remain within Upwork. Do not include an outside email address, telephone number, or meeting link in the public posting.

We are a stealth-mode startup building an innovative AI automation platform for the e-commerce and media technology market.

We are looking for a Senior Full-Stack Engineer to build an isolated media processing and AI data extraction engine as part of our core MVP.

WHAT WE ARE BUILDING (SCOPE):
You will be responsible for developing a standalone Python or Node.js module that accepts short user video inputs, processes frames using computer vision (FFmpeg/OpenCV), transcribes audio (Whisper API), and utilizes Multimodal AI models (OpenAI GPT-4o / Gemini 1.5 Flash) to generate structured JSON data outputs.

WHAT IS ALREADY PREPARED:
- Fully structured Technical Specification & PRD hosted on GitHub.
- Clear API schemas, JSON outputs, and architecture guidelines.
- Standardized IP Assignment & Confidentiality Agreement.

REQUIRED TECHNICAL EXPERTISE:
- Backend: Python (FastAPI / Django) OR Node.js / TypeScript.
- AI & Computer Vision: OpenAI API (GPT-4o, Whisper), Gemini API, FFmpeg / OpenCV for frame extraction.
- Browser Automation (Bonus): Hands-on experience with Puppeteer, Playwright, or Selenium.
- APIs & Cloud: RESTful APIs, PostgreSQL/Redis, Docker.

PROJECT TERMS & PROCESS:
- Project Type: Fixed-Price MVP development (with potential long-term contract / Lead Dev role).
- Estimated Timeline: 3 to 4 weeks.
- Workflow: A 1-week paid test sprint will be conducted with shortlisted candidates.
- Confidentiality: NDA and IP Assignment Agreement MUST be signed prior to full PRD and repository access.

HOW TO APPLY:
1. Briefly describe your experience with AI API integrations, video frame extraction, or web automation.
2. Provide 1–2 links to relevant past projects, live demos, or your GitHub profile.
3. Confirm your availability to start within the next 3–5 days and your willingness to sign an NDA/IP agreement.

Senior AI Full-Stack Developer(s) Needed – Enhance Existing AI Production Pipeline (MVP 2)

Please Read Before Applying

Applicants based in Europe only. Due to communication, collaboration, and time zone requirements, we are currently only considering developers located in Europe.

Please only apply if you are confident you can complete the project within the stated budget.

The budget for this project is fixed and we are not open to phased deliveries, milestone reductions, or higher budget proposals. If your approach requires additional funding or splitting the project into multiple paid phases to deliver the requested scope, this project is not the right fit.

We value your time as much as our own, so please only submit a proposal if you can deliver the requested scope within the posted budget.

Project Overview

We are looking for 1–2 experienced AI developers to enhance an existing AI Production Pipeline (MVP 1) into MVP 2.

This is not a greenfield project or a complete rebuild. MVP 2 focuses on enhancing the existing production pipeline by improving automation, production quality, synchronization, visual consistency, and scalability while preserving the stable systems already implemented.

A very detailed full scope specification will be provided to shortlisted candidates.

Scope of Work

The project includes enhancing and integrating multiple production engines, including:

- Series Script Adaptation Engine
- Series Audio Script Engine
- Cinematic Storyboard Engine
- Timing & Synchronization Engine
- Digital Actor System
- Environment Management System
- AI Image Generation Engine
- AI Video Generation Engine
- Lip Synchronization & Performance Engine
- Episode Assembly
- Promotion Engine
- End-to-end production workflow integration
- Testing
- Deployment
- Documentation
- Knowledge Transfer


What We're Looking For

We're looking for developers with experience in:

- Generative AI
- Large Language Models (LLMs)
- AI Image Generation
- AI Video Generation
- Computer Vision
- AI Workflow Automation
- Python
- Backend Development
- API Integration
- Software Architecture
- Cloud Infrastructure (AWS/GCP)
- Docker
- FFmpeg

Experience building AI content creation platforms, media generation pipelines, or similar AI production systems is highly preferred.


Budget

Fixed Price: USD $2,000


Timeline

Target delivery: 5-6 weeks
Maximum delivery time: 6 weeks

Only apply if you or your 1–2 person development team can realistically commit to this timeline and deliver the complete project within the stated budget.


Before Hiring

Shortlisted candidates must provide:

- Technical implementation approach
- Proposed system architecture
- Development methodology
- Weekly milestones
- Technology stack
- AI providers and third-party services
- Infrastructure design
- Testing strategy
- Risk assessment
- Development structure
- Estimated development hours
- Operating cost estimates


To Apply

Please include:

1. Similar AI projects you have completed.
2. Your proposed technical approach.
3. Whether you are applying as 1 developer or a 2-person team, and each person's role.
4. Estimated development hours.
5. Availability over the next 6 weeks.
6. Portfolio, GitHub, or previous work.
7. Any recommendations or concerns after reviewing the project.

Generic proposals will be declined. We are looking for experienced professionals who can clearly explain how they would approach enhancing and integrating an AI production pipeline of this scope while delivering within the stated budget and timeline.

We are a specialized AI/ML engineering team working on a high volume of complex projects across diverse industries (including Health Tech, AgTech, and Industrial Automation). As our project pipeline grows, we are looking to expand our core team with a highly skilled and hands-on Computer Vision Engineer.
This is an ongoing, long-term opportunity to join a collaborative environment and work alongside other senior AI professionals to tackle real-world vision problems, from 2D object detection to 3D image segmentation and feature extraction.

Core Responsibilities:

- Collaborate with our existing engineering team on the design and implementation of computer vision pipelines (Object Detection, Semantic Classification, Image Segmentation).
- Train, fine-tune, and evaluate deep learning models using PyTorch and TensorFlow.
- Perform data preprocessing, augmentation, and handling of large-scale image/video datasets.
- Optimize models for inference and assist in integrating computer vision APIs into our broader software architecture.
- Write clean, well-documented, and production-ready Python code while participating in code reviews with the team.

Mandatory Skills & Expertise:

- Strong proficiency in Python and core computer vision libraries (OpenCV).
- Proven hands-on experience with PyTorch (preferred) or TensorFlow.
- Solid background in implementing architectures like YOLO, SSD, or similar for object detection/segmentation.
- Experience with deep learning model evaluation and performance tuning.

Nice to Have:

- Experience with 3D Reconstruction, Stereo Matching, or Multi-view Geometry.
- Knowledge of C++ and CUDA optimization.
Familiarity with SLAM, Robot Operating System (ROS), or autonomous vehicle applications.

We are seeking a skilled freelancer to assist in building an AI data hub center in the Northeast. The project involves developing a comprehensive platform for data management and AI integration. The ideal candidate will have experience in AI technologies and data infrastructure development. This is a part-time role with a short-term engagement.

We are looking for freelancers, recruitment agencies, and teams to support a paid AI data collection project by recruiting participants and/or completing recordings.

This project helps build next-generation AI, Robotics, and Computer Vision systems through first-person (egocentric) video recordings.

Currently Recruiting Participants From
Mexico
Brazil
Argentina
Colombia
Chile
Peru
Ecuador
Costa Rica
Panama
Uruguay
Guatemala
Dominican Republic
Project Overview

Participants will record everyday activities using a head-mounted smartphone to capture a first-person perspective.

Both Residential and Commercial recording tasks are available.

Required hours - 400
400 hr X $5 = $2000

Device Requirements (Mandatory)

Applicants must own one of the following smartphones:

Apple iPhone 12 or newer
Google Pixel 6 or newer
Samsung Galaxy S21 or newer

A compatible head mount is required to securely attach the smartphone and record from a first-person perspective. Participants without a head mount are not eligible for this project.

Compensation
$4–$5 USD per approved recording hour
Payment is based on approved recordings that meet the project quality requirements.
For Freelancers
Complete recordings yourself if you meet the requirements.
Or recruit eligible participants and manage the recording process.
For Agencies & Teams

If you already have a team or community of contributors, you're welcome to apply.

We will pay your agency/team lead directly, and you can manage payments to your own team members. This is a great opportunity for agencies, outsourcing companies, and community managers with an existing contributor network.

What We're Looking For
Reliable communication
Ability to follow project guidelines
High-quality recordings
Timely delivery
How to Apply

Please include the following in your proposal:

Your country
Whether you are applying as an Individual or an Agency
Number of contributors you can provide (if applicable)
Smartphone models available
Whether you already have compatible head mounts
Your estimated weekly recording capacity

We are onboarding contributors immediately and look forward to working with reliable freelancers and agencies across the Americas.