Discover Computer Vision Projects Efficiently

⚽ Project Opportunity: Next-Gen Soccer AI Commentary System (Computer Vision Lead) 🚀

I am managing a high-profile project to build a next-generation, context-aware soccer AI commentary system from scratch. I am looking for a elite Computer Vision (CV) Partner (individual or small specialized team/lab) to contract with me and own the entire vision architecture and codebase.
As the Project Lead, I will act as the sole buffer and handle all client-side communications and requirements. This allows you to focus purely on development. I will also be handling the Generative AI infrastructure, while you will have complete ownership of the Vision data layer.

📌 Preferred Partners:
We are looking for highly specialized, autonomous technical talent who can architect a production-ready CV codebase from day one. We highly prefer:
Academic Labs, Research Teams, or PhDs specializing in sports analytics or spatio-temporal video analytics.
Small specialized startups or agile teams (2–3 members) capable of co-owning this architecture.
Industry Professionals/Employees currently working in football analytics and vision AI. Both part-time collaboration and full-time contract arrangements are fully welcome (provided it does not violate any current employment laws or company policies).

🛠️ Scope of Work (Your Complete Ownership):
Robust multi-object tracking (player and ball detection across broadcast video).
Spatio-temporal action spotting and automated match event recognition.
End-to-end broadcast video-to-data pipelines.

💼 Project Terms & Budget:
Timeline: Strict 3-month schedule from kickoff to delivery.
Budget: ~$50,000 USD.
Contract Structure: We will execute a secure Individual-to-Individual (or Individual-to-Team/Entity) contract with a milestone-based payout structure.
Condition: The full budget is locked and will be rewarded upon delivery of a highly competitive, state-of-the-art result.

#ComputerVision #SportsTech #MachineLearning #DeepLearning #VideoAnalytics #AIContractor #SportsAnalytics #TechHiring

Project Description:

We are looking for a freelance computer vision developer to design a real-time cattle tracking and counting system using a fixed camera installed in a livestock building.

The main objective is individual animal tracking: each bovine must be assigned a unique and persistent identifier to ensure reliable counting without duplicates (even in the event of crossing paths or leaving/re-entering the camera's field of view), as well as movement statistics (density heatmap, time spent in each zone).

Project Scope:

- Cattle detection using YOLOv8 (or equivalent SOTA model)
- Multi-object tracking (ByteTrack or BoT-SORT) to assign a unique and persistent ID to each animal
- Handling of occlusions and re-identification after temporary loss of tracking
- Generation of a heatmap/density map based on tracked paths
- Real-time counting without duplicates (target more than 15 FPS)
- Web dashboard (Flask) displaying the video stream annotated with IDs, the live count, and the heatmap
- Deployment on edge hardware (Raspberry Pi 4 or Jetson Nano) for 100% local processing

Expected Deliverables:

- Trained YOLOv8 model (.pt + .onnx)

- Configured and calibrated tracking module
- Documented Python script (complete pipeline: capture → detection → tracking → counting) → heatmap)
- Functional Flask dashboard
- Export of tracking data (CSV/JSON: positions, trajectories, counts)
- Technical documentation and deployment guide
- Source code on a Git repository

Required skills:

- Python, OpenCV
- YOLOv8 / Ultralytics
- Multi-object tracking algorithms (ByteTrack, BoT-SORT, DeepSORT)
- Flask or equivalent lightweight web framework
- Experience deploying on embedded hardware (Raspberry Pi / Jetson Nano) is a plus

To apply, please provide:

- Your previous projects involving object detection and/or tracking (links or examples welcome)
- Your approach to preventing ID switches in a dense herd
- An estimated timeframe and budget for this project

Role Overview
We are seeking a talented Python Developer with strong expertise in AI and ML to join our team. This position will work collaboratively with key members of the AI development team responsible for growing our agent-driven platform.

In this role, you will work on our core platform, which utilizes MCP (Media Content Platform) with proprietary agents, integrated seamlessly with Langchain and Retrieval-Augmented Generation (RAG) techniques. You will contribute to building and maintaining AI-powered automations, handling extensive API integrations, and leveraging Blender libraries for 3D modeling and rendering tasks. This position is ideal for someone passionate about applying AI in creative industries, with a focus on automation, generative tools, and specialized ML models.

Key Responsibilities
• Design, develop, and maintain Python-based applications and scripts that integrate MCP with proprietary agents for AI-driven workflows in media and entertainment.
• Implement and optimize Langchain integrations, including RAG pipelines, to enhance data retrieval, processing, and generation for content automation.
• Build and manage complex API integrations to connect our AI systems with external services, ensuring seamless data flow and automation across platforms.
• Develop and deploy AI/ML models for automation, including generative AI tools and specialized models tailored to media/entertainment use cases such as content recommendation, asset generation, and workflow optimization.
• Collaborate with cross-functional teams (e.g., data scientists, designers, and engineers) to troubleshoot, debug, and scale AI integrations.
• Conduct code reviews, ensure best practices in Python development, and contribute to architectural decisions for scalable, maintainable systems.
• Stay updated on emerging AI/ML trends, particularly in GenAI and automation, and apply them to improve our proprietary systems.

Required Qualifications
• 7+ years of professional experience as a Python developer, with a proven track record in AI/ML projects.
• Strong proficiency in Python, including libraries such as NumPy, Pandas, Scikit-learn, TensorFlow/PyTorch, and experience with API development (e.g., RESTful APIs, FastAPI/Flask).
• Hands-on experience with Langchain, RAG techniques, and building agent-based systems.
• Solid understanding of AI for automation, including generative AI (e.g., Stable Diffusion, GPT models) and specialized ML models.
• Excellent problem-solving skills, with the ability to handle complex integrations and debug AI-driven systems.
• Strong communication skills and a collaborative mindset for working in a fast-paced team environment.

Preferred Qualifications
• Experience with MCP or similar proprietary agent-based platforms in AI contexts.
• Knowledge of cloud services (e.g., AWS, GCP) for deploying AI/ML models.
• Background in media-specific tools and libraries (e.g., OpenCV, FFmpeg).
• Familiarity with Blender's Python API and scripting for automation in 3D environments.
• Contributions to open-source AI/ML projects or publications in relevant fields.
• Familiarity with DevOps practices, including CI/CD pipelines and containerization.

What We Offer
• Competitive salary or hourly rate.
• Opportunities to work on innovative projects in media and entertainment sector.
• A collaborative, inclusive work environment with flexible remote/hybrid options.
• Professional development resources, including access to AI/ML tools and training.
• The chance to make a real impact by shaping the future of AI-driven media automation.

If you are a Python expert passionate about AI/ML in creative industries, we encourage you to apply. Please submit your cover letter highlighting your relevant experience.

About MetaBull Entertainment
MetaBull Entertainment has automated much of the CGI process for film and television. Our platform, Felix (v2), combines models created by artists with our proprietary agent-driven workflows, real-time viewports and multi-shot conversational AI to deliver unparalleled creative control and efficiency. Backed by a global team of engineers, scientists and filmmakers, we prioritize ethical content creation—rigorously protecting licensed assets and intellectual property. Committed to democratizing production, we empower a diverse creative community with accessible, professional-grade tools that streamline workflows and elevate visual storytelling.

We are seeking an experienced video annotation specialist to label American football players in one video clip that includes all 22 players for a computer vision project.

The objective is to create high-quality player tracking data by drawing accurate bounding boxes around every on-field player and maintaining consistent player identities throughout each video.

This project requires exceptional attention to detail.

• Describe how you prevent ID switches when two tracked objects overlap or become occluded.

I am looking to start an AI Agent business to sell to businesses. I need someone who can build an AI agent that I can sell and build a business around. The ideal candidate will have experience in AI development and business strategy to ensure the AI agent is scalable and marketable.

We are hiring an AI Engineer for a remote opportunity with our Airlines project. The ideal candidate should have hands-on experience building GenAI solutions, including RAG pipelines, vector embeddings, prompt engineering, MCP server development, and integrating multiple LLM providers. Experience working with AWS Neptune (Graph DB), OpenSearch (Vector Store), Redis, REST APIs, and SSE-based streaming services is required. Exposure to LangChain, MCPSharp, or ModelContextProtocol.SDK is a plus. If interested, please share your updated resume along with your total years of experience, years of GenAI experience, RAG experience, MCP/Agentic AI experience, current location, work authorization, and availability to start.

• How many years of experience do you have with RAG (Retrieval-Augmented Generation) development?
• How many years of experience do you have building AI orchestration / agentic workflows / MCP servers?

# Senior iOS / Swift Engineer — On-Device ML + Deterministic, Tested Systems

We're building the on-device engine at the core of a consumer product, and we need a senior iOS engineer to own it. This is the technical heart of the system — a long-term, substantial engagement for the right person, and the most important hire on the project.

## What you'd build

An engine that takes a stream of recognition readings and turns them into a reliable, live model of a changing state — then drives data-driven responses from that state. Three kinds of engineering meet here:

- **On-device ML inference** — running trained Core ML models on a live input stream and building a robust pipeline around their imperfect outputs.
- **Deterministic state & logic** — turning noisy, reading-by-reading inputs into a confidence-scored model over time, then applying provable, fully-tested rules on top of it.
- **Data-driven content playback** — a templating engine that selects and fills pre-written content based on live state, without repeating itself.

**The hard part — and the thing we're really hiring for:** individual readings are sometimes wrong. The engine must never trust a single reading. It builds confidence over many of them, treats disagreement between readings as uncertainty rather than guessing, and tracks state region-by-region so a gap in part of the input doesn't blind the whole system. If you've built something that stays reliable on top of unreliable inputs, that is exactly the experience we want.

## Hard requirements (non-negotiable)

- **Senior Swift** — real depth: Codable, generics, async/await, Swift packages. Not someone learning Swift on the job.
- **Core ML in production** — you have integrated and run trained models on a device and understand the memory and performance side. (Running models is the requirement; training them is a bonus.)
- **Fully offline during a session** — zero network calls. This is both a privacy guarantee and a hard architectural rule. (Content updates happen between sessions, never during one.)
- **Runs on modest hardware** — must perform on an iPhone 11 / 12 class device, for hours, with no thermal throttling and no battery drain. Real performance discipline required.
- **Deterministic and tested** where it counts — the logic and playback layers must be reproducible and covered by tests.

## Please read before applying

- There is **no large language model** anywhere in this system. The content is pre-written and played back by a deterministic templating engine — not generated live. If your instinct for the responses layer is to drop in an on-device or cloud LLM, this is not the right role for you.
- This is **not** a cross-platform job. No Flutter, no React Native. Native Swift only.
- This is **not** a "call a cloud AI API" job. Everything runs natively, on-device, deterministically. If your AI experience is entirely cloud API calls, this role is the opposite of that.

## Strong pluses

- On-device computer vision familiarity (Vision framework, turning image data into structured readings).
- Game development, simulation, robotics, or other serious deterministic-systems background.
- Plugin systems, content pipelines, or data-driven engine architecture (you've built a system where non-engineers add content without touching engine code).
- Dynamic text, dialogue, or localization-engine experience.

## How to apply — please answer these in your proposal

Skip the generic pitch. I can tell, and it tells me nothing. Instead, answer these four questions **in your own words** — short and concrete is better than long and polished. Honest answers from real experience are what I'm reading for, so please write them yourself rather than having a tool do it; I'm hiring your thinking.

**1.** You get one recognition reading every few seconds, and individual readings are sometimes wrong. How do you build a reliable picture of the overall state from unreliable readings over time? Tell me how you'd actually structure it.

**2.** You need a recognition model plus a multi-hour, continuously-running inference loop to fit inside an iPhone 11's memory and heat budget. What do you measure first, and what do you cut first when it doesn't fit?

**3.** Briefly: describe a real system you built where correctness *had* to be provable — or where you made something reliable on top of unreliable inputs. What was the problem, and what did you actually do? (One you can point to or talk through in a call is ideal.)

**4.** Have you shipped an app that ran a Core ML model on-device? Name it (or describe it), and tell me in one or two sentences how you handled the model's memory and performance cost.

A note on what good looks like: I'm looking for specifics — what you'd profile, where you'd cut, a time you got a CPU-vs-GPU or memory call *wrong* and how you knew. Vague reassurance that you "optimize for performance" is a pass-over. A short, plain, real answer beats a confident generic one every time.

## The shape of the engagement

This starts with a **small, paid proof-of-concept** that tests the make-or-break skill on a modest device — nothing product-specific, just the core of the work — before any larger commitment. If that goes well, this becomes the most substantial and longest engineering relationship on the project. I'd rather pay for proven seniority and a track record of shipped, tested, on-device work than gamble on someone unproven.

If this is the kind of careful, provable, on-device systems work you actually enjoy, I'd like to hear from you.

Job Title:Backend Engineer Needed for Private B2B Procurement Portal & Media Pipeline (2-Week Timeline)Job Description:We are seeking an expert Backend / Full-Stack Developer to build a private procurement and inventory asset platform for an industrial goods distributor (35,000 SKUs). We have 50 active users waiting to onboard, so this project requires a compressed, phased 2-week timeline.This role focuses heavily on relational database management, third-party data synchronization, and automated image processing pipelines.📋 Phased Deliverables:Phase 1 (48 Hours): Deploy a Minimum Viable Product (MVP) portal using Glide (Business Plan) with a static template list of 500 catalog rows so users can manually log interaction requests immediately.Phase 2 (Days 3–7): Build the private master database using Supabase or PostgreSQL. Design automated script pipelines to batch-update pricing data on a quarterly cycle from provided supplier data sheets and web endpoints.Phase 3 (Days 7–10): Build an automated Python media processing pipeline (using Pillow/OpenCV). The system must source missing part graphics via image search APIs, normalize image resolutions, auto-crop backgrounds/overlays, and programmatically apply our transparent company watermark.Phase 4 (Days 11–14): Program our masked profit margins (+15%) into the frontend view, optimize database queries to comfortably support 50+ concurrent users, and integrate order-routing queues via Make.com.🛠️ Required Technical Skills:Advanced Python & backend automation workflows.Image manipulation and computer vision libraries (OpenCV or Pillow).Database Architecture (Supabase / PostgreSQL) optimized for 35,000+ rows.No-Code Frontend Deployment (Glide).API Integrations (Make.com).

I am the founder of a hardware startup scaling a new IoT invention (smart thermal printers) to millions of global physical endpoints. I need an elite, senior-level AWS architect to design and deploy the cloud infrastructure.

The Workflow:
Millions of physical devices will send files to our AWS backend. An AI computer vision model (YOLO) will process and crop the image in real-time, and return the payload to the physical device for printing. This must be imperceptible to the user.

Requirements:

Infrastructure as Code (IaC): 100% of the environment must be built using Terraform. No manual console deployments.

Global Edge Routing: Experience with AWS Route 53, Global Accelerator, and API Gateway for ultra-low latency.

MLOps / GPU Auto-scaling: You must know how to deploy computer vision models (e.g., using SageMaker or EKS) on GPU instances (g4dn/T4) and configure dynamic auto-scaling to keep costs down during low traffic.

IoT Security: Experience with AWS IoT Core and managing mutual TLS (mTLS) for hardware authentication.

High Availability: Must architect an active-active multi-region failover. If US-East drops, the printers cannot go offline.

To Apply:
Please begin your proposal with the phrase "Zero Downtime." Include a brief overview of how you would handle the GPU auto-scaling for the YOLO model, and confirm your experience writing Terraform for AWS IoT deployments. Must hold an active AWS Solutions Architect Professional certification.

We are seeking an experienced programmer to develop and implement AI and Computer Vision solutions. The ideal candidate will have a strong background in machine learning and image processing, with the ability to design and optimize algorithms for various applications. Responsibilities include developing AI models, integrating computer vision into existing systems, and collaborating with cross-functional teams to enhance product capabilities.

I am putting together an instructional animation and need a five-person AI task-force to power the technical side of the production. The core roles are Machine Learning Experts, Data Scientists, and Computer Vision Specialists—ideally people who feel at home moving between Python notebooks, TensorFlow / PyTorch stacks, and OpenCV pipelines.

Your main focus will be data analysis and algorithm development. Concretely, I’ll count on you to:

• Analyse raw video and motion-capture data so we can pinpoint the most engaging sequences to animate.
• Develop custom algorithms that automate character rigging, lip-sync, and scene transitions, feeding those outputs directly into our animation software (Blender and After Effects).
• Optimise and document the entire workflow so our creative team can reproduce results without heavy technical hand-holding.

I’ll provide the datasets, style guidelines, and a shared Git repo; you bring reproducible code, clear comments, and model checkpoints ready for review. Once your contributions plug seamlessly into the animation pipeline and the test renders pass quality checks, the job’s done.

If collaborating on an AI-driven instructional video excites you, let’s talk timelines and milestones.

Skills: Python, CUDA, Machine Learning (ML), C++ Programming, Data Science, OpenCV, Data Analysis, Computer Vision

We are seeking a skilled freelancer to generate high-quality AI images for a fashion brand at scale, must have a good eye for aesthetically pleasing images. Must have experience using freepik, higgsfield or similar and be up to date on the best models/ chat images 2.0 etc.

• Have you used any image or video generation apps before. If so which ones.

We are looking for a skilled and reliable Video Data Annotation Specialist to join our AI/ML project on a long-term or project basis. This is a private job for high-quality data labeling work focused on video content.

Project Overview:
Annotate videos for computer vision and machine learning model training.
Tasks include object detection, tracking, segmentation, action recognition, bounding boxes, polygons.

Responsibilities:
Accurately label objects, events, and behaviors in video frames.
Maintain high annotation quality and consistency across large video datasets.
Follow detailed annotation guidelines and quality control processes.

Nice-to-Haves:
Experience annotating specific domains (e.g., medical, automotive, human pose estimation).
Familiarity with quality assurance processes in data labeling.
Availability for full-time or high-volume part-time work.

Please apply with:
A brief introduction and your relevant experience in video/data annotation.
Links to similar past projects or portfolio samples.
Your expected hourly rate or availability.
Confirmation of tools you are proficient with.

PROJECT CONTEXT

We are building a Computer Vision system for video analysis. We need to hire 2 profiles:
1. ARCHITECT (technical design, decisions, oversight)
2. DEVELOPER (implementation, coding, optimization)

This is a 6-8 week MVP project to build the FOUNDATION of our system (foundation + basic detection/tracking).

After Phase 1 validation, we may extend with future phases.

CRITICAL POINT

This project CANNOT be solved with:
- LLM (ChatGPT, Claude, Gemini)
- Generic vision APIs (Google Cloud, Azure)
- Low-code solutions or templates
- "Quick solutions"

This project REQUIRES:
- Real expertise in classical Computer Vision
- Deep Learning (PyTorch or TensorFlow)
- Proven experience: object detection + video tracking
- GPU optimization (NVIDIA CUDA)
- Production-grade code

TECHNICAL ARCHITECTURE

MVP Phase 1 = 2 main components:

1. VIDEO PIPELINE FOUNDATION
• Optimized video-to-frames decomposition
• Robust extraction and preprocessing
• Memory management for video streams
• Stack: OpenCV, FFmpeg, NumPy

2. DETECTION + TRACKING INTEGRATION
• YOLO v8+ for object/person detection
• DeepSORT for temporal tracking
• Frame-to-frame ID association
• Real-world condition reliability
• Stack: YOLO (Ultralytics), DeepSORT, PyTorch

MVP PHASE 1 TIMELINE

DURATION: 6-8 weeks total

WEEK 1-2: Architecture & Setup
- System design and technical decisions
- Development environment setup
- Testing framework design
- Performance benchmarking plan

WEEK 3-4: Video Pipeline
- Frame extraction pipeline
- Preprocessing and optimization
- Performance tests

WEEK 5-6: Detection + Tracking
- YOLO integration
- DeepSORT implementation
- ID management and stability

WEEK 7-8: Integration & Polish
- End-to-end testing
- Performance optimization
- Documentation and handoff
- Code cleanup and tests

DELIVERABLE: Working MVP with clean code + documentation

TECHNICAL STACK

Languages:
- Python 3.9+ (mandatory)
- C++ (optional, optimization)

Deep Learning & CV:
- PyTorch (preferred) or TensorFlow
- Ultralytics YOLO
- DeepSORT
- OpenCV, NumPy, scikit-image

Infrastructure:
- NVIDIA CUDA + cuDNN
- GPU memory optimization
- Docker (containerization)
- Git (version control)

WE'RE LOOKING FOR 2 PROFILES

PROFILE 1: ARCHITECT
- 5+ years Computer Vision experience
- Experience designing CV systems from scratch
- Strong understanding of YOLO, tracking algorithms
- Can make technical trade-off decisions
- Proposes optimizations and alternatives
- Oversees code quality and architecture
- Part-time or full-time OK (oversight role)

PROFILE 2: DEVELOPER (Senior)
- 3+ years production Computer Vision experience
- Expert in YOLO, OpenCV, PyTorch
- Experience with DeepSORT or similar tracking
- Strong Python + GPU optimization skills
- Can write clean, tested, production code
- Follows architectural decisions
- Full-time commitment preferred for 6-8 weeks

APPLICATION INSTRUCTIONS

Specify which role you're applying for: ARCHITECT or DEVELOPER

1. PORTFOLIO (mandatory)
- 2-3 CV projects you've delivered
- GitHub repos OR description + results
- Technologies used, your exact role

2. EXPERIENCE
- Years in Computer Vision (total)
- YOLO / object detection (versions)
- Tracking experience (DeepSORT, centroid, etc.)
- PyTorch/TensorFlow experience
- GPU optimization experience
- Production projects (not just Kaggle)

3. TECHNICAL OPINION
- Feasibility of MVP in 6-8 weeks: realistic?
- Any concerns or risks?
- Your approach to Phase 1?

4. TEAM PREFERENCE
- Have you worked with an architect/developer pair before?
- Preference for sync or async communication?

EXPECTED DELIVERABLES

CODE:
- GitHub repo (private/shared)
- Clean, tested Python code
- Unit tests (pytest)
- Requirements.txt / poetry.lock
- Meaningful docstrings

DOCUMENTATION:
- Architecture README (with diagrams)
- Installation guide
- API / usage examples
- Performance benchmarks (FPS, memory)
- Known limitations

HANDOFF:
- Code review completed
- Setup verified on clean machine
- Performance validated

FAQ

Q: Can one person do both (architect + developer)?
A: Possible for strong senior, but better with 2 people. More progress, better oversight.

Q: What YOLO version?
A: Open. v8 good starting point, v10 or ByteTrack alternatives welcome if justified.

Q: After MVP, what happens?
A: We evaluate quality. Strong MVP = potential Phase 2-4 continuation.

Let's build something solid!

• Describe your recent experience with similar projects

I’m assembling a worldwide team to design and deploy next-generation intelligent systems. My own focus is Artificial Intelligence, and every project on our roadmap—from rapid prototypes to production-grade platforms—relies on solid expertise in the field.

The core technical stack revolves around:
• Machine Learning
• Natural Language Processing (NLP)
• Computer Vision

If you work comfortably across those areas and can demonstrate real-world results, we should talk. Contributions may range from model architecture and data-pipeline design to API integration and performance tuning; exact scopes will be matched to each contributor’s strengths.

When you send your proposal, highlight the experience that best proves you can deliver impact quickly (publications, shipped products, open-source commits, etc.). I’ll review backgrounds, share full project specs with shortlisted specialists, and together we’ll outline milestones, code-review protocols, and testing standards.

This is an opportunity to push technological boundaries alongside a collaborative, supportive group. Let’s build the future of intelligent systems together.

Skills: C Programming, Java, Python, Engineering, Computer Vision, Natural Language Processing, AI Model Development, AI Development

We are looking for an experienced, self-motivated WordPress developer to manage and grow strandalert.com.
Strand Alert promotes advanced computer vision technology (IntelliC / INTELLISE SE) designed to detect and recognize security risks in public spaces. The website serves as our primary information and marketing platform.
What We Need:
• Ongoing WordPress maintenance and optimization
• Strong content writing and editing skills (fluent English required)
• Ability to research AI, security, and tech topics independently
• Proactive mindset — minimal supervision required
• SEO knowledge and performance improvements
This is not just a technical role — we want someone who can take ownership of the site, improve

We are hiring contributors for an international AI & Robotics Video Data Collection Project.
Participants will record natural first-person POV videos using a smartphone mounted on a forehead/head strap while performing daily household activities.

Eligible Countries

USA,Mexico, Colombia,Argentina,Brazil

Eligible Devices
📱 iPhone 11 to 17 series
(Except iPhone 16e & 17e)
📱 Google Pixel 6 to 9
📱 Samsung Galaxy S21 to S24

Project Scope
Contributors will record activities such as:
Cleaning
Washing dishes
Organizing household items
Daily home activities

Requirements
✔ Good internet connection
✔ Head mount strap
✔ Good lighting
✔ Natural movement during recording
✔ Both hands visible in frame

Key Highlights
Fully remote
Flexible schedule
Training provided
No audio recording
Opportunity for long-term collaboration
Interested candidates can apply by sharing:
Country
Phone model
Availability
Relevant experience (if any)

Job Title: Senior Embedded Hardware & Vision Engineer — Drone EO/IR Payload
Project: Building a NextVision-class EO/IR camera payload for defence UAVs (Indian market)

Must Have

PCB design in Altium or KiCad — 6 to 10 layer, high-speed, impedance controlled
MIPI CSI-2 sensor bring-up on Linux (V4L2 drivers)
Experience with Sony IMX or FLIR Boson/Lepton integration
NVIDIA Jetson Orin or equivalent SoC — board bring-up, BSP customization
Basic ISP pipeline — exposure, white balance, noise reduction, HDR
H.264/H.265 hardware encode, RTSP streaming via GStreamer
Power electronics — multi-rail DC-DC design from 12V or 28V UAV bus
EMI/EMC layout discipline


Good to Have

Gimbal controller electronics — FOC/BLDC, SimpleBGC or custom
TensorRT or OpenCV deployment on edge
MISB ST 0601 metadata embedding
Experience with defence or aerospace payloads


Deliverables Expected

Sensor interface PCB schematic and layout files
Working video stream from EO and/or IR sensor on Jetson
H.265 encoded RTSP output with stable latency under 150ms
Basic ISP tuning for outdoor UAV lighting conditions


Experience Required: 8+ years in embedded systems with at least 2–3 years specifically in camera/vision hardware
Engagement: Freelance, milestone-based, long-term if fit is right

I am looking for a Python developer with expertise in GIS/Digital Mapping and Computer Vision to build a custom property lead-generation pipeline for the UK market.

The goal of the tool is to identify specific building characteristics (large, blank vertical facades) across targeted commercial routes in Manchester, UK.

Project Workflow & Requirements:

Data Pipeline: Connect to the official Google Street View API to evaluate location metadata and analyze street-level perspectives along a provided list of Manchester A-road coordinates.

Computer Vision Analysis: Process the image stream using an object detection model (such as YOLOv8/v11 or OpenCV). The model needs to analyze the building facades to detect large, continuous surfaces while filtering out buildings with heavy window density, obstructions, or complex architecture.

Compliance Filters: Cross-reference the coordinates with open UK heritage databases to ensure flagged locations are not Grade II listed.

Output: Structure the validated data into a clean Google Sheet containing the Street View coordinates, address, and a link to the UK Land Registry.

Milestones:

Milestone 1 ($200): Successful API pipeline development and routing configuration.

Milestone 2 ($200): Computer Vision logic integration and Google Sheets output optimization.

Please include the word "MANCHESTER" at the top of your proposal.

AI performance intelligence company building the infrastructure layer for human movement in elite sport. Our system uses computer vision to identify athletes, recognise exercises, and deliver real-time coaching feedback simultaneously across an entire training environment.

You will own the technical blueprint that everything else is built on — the foundation that makes multiple simultaneous institutional deployments possible, that connects our edge AI layer to our cloud platform, and that allows a small engineering team to move fast without accumulating debt.
You will work directly with the CPO and alongside a team of backend, frontend, and AI engineers, and in close collaboration with our AI contractor team in Slovenia. You define the standards, the patterns, and the decisions that make the difference between a product that scales and one that breaks at the wrong moment.

Designing and owning the end-to-end system architecture across edge devices, cloud infrastructure, mobile, and web platform. Defining integration patterns between our AI inference layer (running on edge hardware) and our centralised cloud services. Establishing the data architecture for real-time movement data, athlete profiles, and performance longitudinal records. Setting engineering standards — code quality, testing, deployment, documentation — for a team that is growing fast. Driving the technical integration for our telco white-label product workstream. Supporting the evaluation and onboarding of new engineering hires.

Seven or more years of software engineering experience with at least three in a senior architecture or technical lead role. Demonstrable experience designing distributed systems with both edge and cloud components. Strong grasp of real-time data pipelines and event-driven architectures. Experience with containerisation, Kubernetes, and infrastructure-as-code. Familiarity with AI/ML deployment at the edge is a significant advantage. Experience in sports technology, health technology, or IoT environments is a plus. Strong communicator — you will be explaining architectural decisions to technical and non-technical stakeholders.

Python and Node.js backend. Firebase and Google Cloud infrastructure. React-based web platform. React Native mobile. Edge inference on NVIDIA Jetson hardware.

Skills: Python, Website Design, Graphic Design, Mobile App Development, Cloud Computing, UML Design, Software Architecture, Node.js, Kubernetes, AI Development

Project Description

We are building an early-stage AI-powered vehicle damage assessment startup.

We are looking for an experienced Computer Vision Engineer to develop the core AI engine for our prototype.

We only need:

* AI model training
* Cloud deployment
* API development

Project Scope

1. Prepare and clean vehicle damage datasets.
2. Fine-tune a YOLOv8 / YOLOv11 model (or equivalent) for vehicle damage detection.
3. Detect damaged vehicle parts and classify damage categories.
4. Generate structured JSON output.
5. Deploy the trained model to the cloud.
6. Build a FastAPI endpoint for inference.
7. Provide documentation for API integration.

Deliverables

* Trained model weights
* Source code
* Dataset preparation pipeline
* FastAPI endpoint
* Cloud deployment
* API documentation
* Basic testing/demo interface (optional)

Required Skills

* Python
* PyTorch
* YOLOv8 / YOLOv11
* Computer Vision
* Image Segmentation
* FastAPI
* Cloud Deployment

Nice to Have

* Automotive damage assessment experience
* Custom dataset training experience


How to Apply

Please start your proposal with the word:

“Prototype”

and include:

1. Similar computer vision projects.
2. Experience training custom YOLO models.
3. GitHub profile or portfolio.
4. Estimated timeline.
5. Your estimated budget range for this scope
6. Brief explanation of how you would approach the dataset and model training process.
We are looking for a long-term technical partner for future phases.

I need an AI-driven solution that will remove the manual grind from our image-heavy data entry and management flow. The end product should pull raw image files from a designated source, read the embedded or visual information, classify or tag each file correctly, and populate our existing database automatically. Accuracy is the top priority; any mislabeled or unprocessed images must be flagged for human review rather than slipping through.

Here is what success looks like for me:
• A repeatable training pipeline (Python preferred,TensorFlow or PyTorch are both fine) capable of handling new images as the dataset grows.
• An inference service that runs on-prem or in the cloud—AWS S3 / Lambda is ideal, but I’m open to workable alternatives—that accepts a batch of images and returns structured records (e.g., JSON) in real time or near-real time.
• A lightweight dashboard or logging mechanism so I can monitor processed counts, error rates, and performance.

You’re free to choose the specific computer-vision models—CNNs, transformers, or a hybrid—as long as they meet these acceptance criteria: at least 95 % labeling accuracy on my validation set, end-to-end processing time under two seconds per image, and clear documentation so my in-house team can retrain or extend the model later.

If you have prior experience automating similar workflows for image data, I would love to see a brief outline of the approach you’d take and examples (links or screenshots are fine).

Skills: Java, Python, Data Processing, Software Architecture, Machine Learning (ML), AWS Lambda, Computer Vision, Data Management

I am looking to build an AI-powered system that analyzes real user workflows and helps automate repetitive tasks to improve productivity and efficiency.

> Project Overview

The goal is to create an AI agent that can observe how users work on their computer, understand their workflow patterns, and identify opportunities for automation and optimization.

>Core Concept
Capture user activity using screen recording or interaction tracking
Use AI to analyze and reconstruct the workflow process
Apply process mining techniques to map real-world workflows
Identify bottlenecks, repetitive tasks, and inefficiencies
Suggest or implement automation for repetitive steps
⚙️ Key Features
Screen activity capture (video or event-based logging)
Workflow extraction and visualization
Process mining and task sequence analysis
Bottleneck detection and performance insights
Workflow automation recommendations or execution
AI-based reasoning for task optimization
> Expected Outcome

A smart AI system that can:

Understand how work is being done
Convert raw activity into structured workflows
Improve productivity by reducing manual repetitive tasks
Help businesses optimize internal processes
> Ideal Skills Required
AI / Machine Learning (especially LLMs or behavior analysis)
Process mining or workflow automation experience
Python / Node.js backend development
Computer vision or screen data analysis (optional but preferred)
Automation tools (RPA, Playwright, Selenium, etc.)

Skills: PHP, Website Design, Internet Marketing, SEO, Digital Marketing, Google Ads, Business Development, SaaS

I need a cross-platform mobile application that lets me snap or upload a customer’s photo, pick a fabric from my catalogue and instantly preview how that cloth will look on the person as a shirt, blazer or pair of trousers. The app must run smoothly on both iOS and Android and deliver realistic, well-lit visualisations in real time, so solid computer-vision and generative AI skills are essential.

My workflow in the shop is simple: take the picture, choose the garment type, tap through the available fabrics and share or save the rendered look. An easy way for me to add or retire fabrics and price tags without touching code would be ideal—an in-app or small web dashboard is fine.

Because this tool will be customer-facing, speed and image quality are non-negotiable; I’m looking for crisp edge detection, natural fall of cloth and accurate colour reproduction. If you plan to use TensorFlow Lite, Core ML, or a Unity/AR foundation, let me know—anything that guarantees real-time performance is welcome.

Please deliver:
• Production-ready iOS and Android builds
• All source code and model files
• Setup / deployment notes I can follow on my own devices

I’d like to start immediately and move quickly, so outline your approach, libraries, and a realistic timeline when you reply.

Skills: Graphic Design, Mobile App Development, Android, iOS Development, Computer Vision, Unity, Generative AI

Hello all you talented people!

I am (unfortunately) a non-coder and I need a standalone Python utility script for Windows 11.

I use AI tools to generate artworks of characters holding my custom trading cards (making trading cards is my biggest hobby). The cards they are holding are good quality and but a bit blurry. I have separate, massive, high-resolution 25MB master PNG files of those exact same cards. For example, I can generate an image of a character holding a card called "Toxicant Skull", which looks okay until you zoom in, and want it replaced with the actual PNG of "Toxicant Skull" which is ultra sharp.

I need a program with a simple terminal menu where I select the artwork and select my HD card, and the program automatically stamps the HD card perfectly over the blurry one. I've tried making it myself with Claude Code, both using some "fill" attempt to fill a black or neon blank card, and a visual matching version to replace the blurry art with the matching HD one. Both failed me.

GOALS & REQUIREMENTS:
1. It must automatically detect the card's position, 3D tilt, and angle inside the artwork.
2. It must automatically keep fingers, hands, and hair ON TOP of the new card. I do not want the card pasting over the character's fingers. I will show an example of what a perfect version looks like (as Claude Code did manage to make ONE look great).
3. No ugly black borders or shadow bugs around the rotated card frame. The PNG basically must replace the original card in the artwork, and preserving the aspect ratio of the original PNG of is the most important thing, as I cannot completely control the thickness or aspect ratio of the card generated in the artwork.

A quick note on testing:
I'm looking for a universal tool because I generate new card art all the time! Once you finish the script using the 7-or-so samples, I'll do a quick test-run on 3-of-so "blind" images on my end that the script hasn't seen yet. This is just to make sure the program made works on new arts and not just the ones I provide. If it automatically handles those new images just as perfectly without needing any code changes, then we are completely good to go, and I'll release the payment immediately! In fact, if the script passes my blind tests smoothly on the very first try without needing bug fixes, I am more than happy to add a nice bonus to the final payout, along with a glowing five-star review!

(Note: An AI helped me write the exact technical specs below so you know what tools to use):
- Must use OpenCV (SIFT/ORB and Homography/RANSAC) for visual feature matching alignment.
- Must use alpha channel blending for seamless background borders.
- Must use pixel-disparity illumination-normalized masking for foreground occlusion (no rigid RGB skin thresholds).

Also, as I am not a coder, if there is a better way to do what I am requesting, I am 100% open to hearing a better way to have this done. If you a skilled with coding or using AI code and this program would be better using a different tech method let me know.

Due to Upwork's 10-file limit, I have only attached a few core examples here. As soon as we open a chat room together, I will instantly send you the zipped full folder containing all 10 test artworks and all matching HD card PNG files! It also includes various screenshots of my failed attempts, and the 2 programs made by Claude Code that couldn't get those attempts right.

If this program works beautifully, I am highly interested in keeping you on for future contract work! I have several other ideas for small utility apps and tools to support this card game as it expands.

P.S. - "Bonus Stress Test Mat.png" - This is the ultimate end-game test file. There are 6 different active cards flat on the playmat along with 3 card backs on the playmat, for a total of 9. If your script can loop through and replace multiple cards at once on this mat, that's amazing! But if the script can only handle 1, 2 or 3 hand images right now, that is completely fine and it will NOT be a dealbreaker for the project payout.

I appreciate the time of anyone who tries to help me. This card game is my life's work! :)

We are looking for a specialist with deep hands-on experience in Sony IMX sensor camera tuning on Android devices, specifically targeting smart glasses and wearable hardware.
The focus is on maximizing zoom capability and image quality for long-range subject capture.

Target Hardware:
The primary device is the RayNeo X3 Pro smart glasses (Sony IMX681 sensor). Experience with this device or similar AR/wearable hardware running Android is a strong plus.

What we need:

• Deep expertise in Sony IMX sensor characteristics and tuning, particularly the IMX681
• Camera2/CameraX API configuration to capture at maximum native resolution on wearable hardware
• Zoom pipeline optimization — understanding the optical and digital zoom limits of Sony IMX sensors and how to extract the best possible quality
• Image quality tuning: sharpness, exposure, shutter speed, ISO, and noise reduction for subjects at distance
• Knowledge of sensor-level parameters and vendor-specific camera behavior on Android wearables devices.

Requirements:

• Proven experience with Sony IMX sensor devices, especially smart glasses or wearable cameras
• Deep knowledge of Android Camera2/CameraX API
• Image quality and zoom specialist background
• Familiarity with RayNeo X3 Pro or similar AR glasses is a strong advantage
• Ability to deliver within a tight timeline

Deliverable:
Optimized camera configuration or working APK that maximizes zoom quality and image sharpness on the RayNeo X3 Pro (Sony IMX681), with demonstrable improvement in long-range capture quality.

WHAT WE'RE SEEKING
We are seeking a skilled developer to build an offline, real-time assessment system that analyses a user through webcam and microphone input.
The system should assess visible communication signals such as posture, gaze/head position, facial cues, hand movement, body movement, and voice delivery cues such as pitch, pace, pauses, volume, and intonation.
This is not a cartoon avatar, game character, VR experience, or VFX project. The core deliverable is the local assessment engine. The realistic human-facing interface is a separate presentation layer and should be implemented using the simplest practical method that meets the agreed requirements.

THE MISSION
Build a local computer vision and voice analysis system that processes live webcam and microphone input in real time, converts those signals into assessment-ready features, applies configurable assessment logic, and produces real-time feedback through a realistic human-facing interface.
The human-facing interface may use pre-recorded human video states, a lightweight talking-head interface, a voice-led coach interface, visual overlays, or another local method proposed by the developer.
All execution must run locally. No cloud inference, telemetry, analytics, external callouts, or subscription runtime dependencies are permitted.

TARGET DEVICE
Primary target: Windows 11 consumer hardware.
Must be benchmarked on the agreed target machine.
GPU acceleration may be used where appropriate.
Must not require non-consumer cluster GPUs.
Kinect, depth cameras, or specialist hardware may be proposed only as optional enhancements, not default requirements.

IN SCOPE

- Real-time webcam capture.
- Real-time microphone capture.
- Webcam-based pose estimation / body landmark tracking.
- Face landmark detection.
- Hand landmark tracking where practical.
- Gaze or head-pose estimation where practical.
- Voice feature extraction, including pitch, pace, pauses, volume, energy, and intonation features.
- Feature extraction layer that converts raw landmarks/audio into assessment signals.
- Configurable assessment logic using local JSON/YAML-style configuration.
- Hot reload for assessment logic without restarting the application.
- Real-time feedback events based on the assessment logic.
- Realistic human-facing interface layer.
- Visual debug overlay showing extracted landmarks/features during development and demo.
- Local benchmarking of latency, FPS, CPU/GPU usage, RAM usage, and sustained runtime.
- Offline verification showing no external calls.
- Clear separation between capture, feature extraction, assessment logic, scoring, feedback events, configuration, human interface, and application/UI layers.
- Documented interfaces between major components.
- Setup documentation, run commands, architecture notes, benchmark notes, and known limitations.

TECH STACK
Note: We will mutually agree on any tech stack changes to ensure no risk to target expectations and success factors:
* Visual engine: Unreal Engine 5
* Human character: MetaHuman first; custom rig later only if needed
* Face/lip animation: NVIDIA Audio2Face-3D / ACE Unreal Plugin
* Body/gaze/gesture: Unreal animation state machine
* Real-time audio/session: LiveKit Agents
* Turn-taking: LiveKit turn detector + Silero VAD
* TTS: Benchmark Chatterbox Turbo
* Backend: Existing backend via clean event bridge
* UI: React Tauri 1.8 wrapper - AJ to do vs PYQT6
* External webcam: OBS Virtual Camera only if needed
MediaPipe : Local multi-modal framework that extracts face, iris, hand, and body pose landmarks concurrently using device hardware.​
ONNX Runtime (onnxruntime-gpu): High-performance local inference engine optimized for running .onnx models utilizing Windows CUDA or DirectML acceleration.​
OpenCV (opencv-python): Native computer vision library used for real-time webcam frame acquisition, image preprocessing, and drawing visual debug overlays.​
sounddevice: Thread-safe audio library interfacing with Windows WASAPI/ASIO drivers to stream raw microphone input into NumPy arrays.​
Silero VAD: Ultra-lightweight voice activity detection model running locally via ONNX to segment real-time speech and calculate pauses.​
parselmouth (Praat for Python): Local acoustic analysis library utilizing mathematical signal processing to extract fundamental frequency for pitch tracking.​
scipy.signal & numpy: Scientific computing libraries used to perform instantaneous local RMS calculations for real-time volume and energy tracking.​
opensmile: Standardized feature extraction framework optimized in C++ to capture comprehensive acoustic parameter sets completely offline.​
PyQt6 / PySide6: C++ backed GUI framework providing hardware-accelerated widgets (QMediaPlayer) and thread-safe signaling to drive the presentation layer.​
watchdog: Local file monitoring utility that actively monitors file changes and triggers instantaneous hot-reloads of configuration files.​
psutil: System monitoring tool that queries the local OS kernel to benchmark CPU, RAM, and application process utilization.​
pynvml: Direct local wrapper for the NVIDIA Management Library used to programmatically log real-time GPU and VRAM metrics.

CLIENT TECHNICAL ENVIRONMENT:
* RTX 4080 15GB VRAM
* Ryzen 9 4950H
* 32GB RAM

PROCESSES (to be refined):
- User Experience During Session
app launch -- zoom like experience (see image)

- Post Session
User stats evaluation summary (driven by all of the info captured within this project): BPS, Pros, Cons, Body Language

Settings menu (icon) -
- Content : Load data (subject matter for user to discuss) - Rules config
- Presenter Persona : System Prompt
- LLM: Choose Ollama Model from drop-down list. User can download local models. Show local models downloaded. Can be deleted. Include synch button to fetch latest models/changes.



OUT OF SCOPE
- Cloud computer vision APIs.
- Cloud speech or voice analysis APIs.
- Subscription-based runtime dependencies.
- Telemetry, analytics, or external callouts.
- Enterprise production-grade deployment.
- Full 3D character pipeline unless separately approved.
- Skeletal mesh / rigged character development unless separately approved. (no only head, shoulder, arms only)
- VFX-heavy digital human rendering unless separately approved.
- Kinect or depth-camera dependency as a default requirement. (going into the distance)
- Deepfake-style synthesis unless explicitly approved in writing.
- Multilingual translation.
- Audio transcription beyond what is required for direct voice delivery analysis.
- Emotion recognition claims unless legally, technically, and contractually approved.
- Hardcoded assessment rules where configuration is practical.

IDEAL EXPERIENCE
Computer Vision
- Real-time pose estimation.
- Body landmark / skeletal landmark tracking.
- Face landmark detection.
- Hand tracking.
- Head-pose or gaze-related estimation.
- OpenCV, MediaPipe, ONNX Runtime, PyTorch, TensorRT, or equivalent local tooling.

Audio / Voice Analysis
- Real-time microphone handling.
- Pitch, tempo, pause, volume, energy, and intonation feature extraction.
- Local DSP/audio feature pipelines.
ffmpeg, PyAudio, sounddevice, librosa, torchaudio, or equivalent tooling.

Local ML / Runtime Optimisation
- Running models locally on consumer hardware.
- Optimising for latency and memory usage.
- Practical benchmarking and profiling.
- ONNX, PyTorch, TensorRT, or equivalent runtime choices.

Human Interface
- Video-based human presenter interfaces.
- Lightweight talking-head interfaces.
- Voice-led coaching interfaces.
- Real-time feedback UI.
- Ability to keep the presentation layer separate from the assessment engine.

Architecture
- Clean modular design.
- Testable component boundaries.
- Configuration-driven behaviour.
- Clear handover documentation.
- Practical trade-off decisions.
- Low technical debt MVP delivery.

NON-NEGOTIABLES
Offline Operation
- The system must run locally.
- No required internet connection.
- No telemetry.
- No analytics.
- No subscription runtime dependency.

Performance
- Target end-to-end feedback latency: under 100ms where technically practical.
- Candidate must benchmark actual latency on the agreed hardware.
- Candidate must benchmark memory usage and sustained runtime.
- Candidate must identify any parts that cannot realistically meet the target and explain why.

Architecture Quality
- The assessment engine and human-facing interface must be separate.
- Webcam capture, audio capture, feature extraction, assessment logic, configuration, feedback events, and presentation must not be tightly coupled.
- Assessment rules must be configurable where practical.
- The MVP must be maintainable, modular, and suitable for clean handover.

Communication
- Explain technical decisions clearly. - at least 30min video catch up, everyother day.
- Flag unclear requirements early.
- Flag delivery risks at least one day before they affect delivery.
- Shared-screen review sessions may be required.
- Live walkthroughs of architecture, benchmarks, and code may be required.
- Mutually agree any changes before committing

Output Quality
- AI-assisted coding is permitted up to a maximum of 60%.
- The developer remains responsible for all code quality.
- Code must be lean, modular, testable, and documented.
- No unsupported implementation choices.
- No unnecessary complexity.
- No hidden dependencies.

Handover
- Hand over all source code, assets, configuration files, setup notes, benchmark results, and documentation.
- Provide clear run instructions.
- Provide dependency and licensing notes.
- Provide known limitations.
- Remove project files and assets from supplier infrastructure after handover unless written approval is given.

DEFINITION OF DONE
The project is complete when:
- The application runs locally on the agreed Windows 11 hardware.
- Webcam and microphone input are captured and processed in real time.
- The system extracts pose/body landmarks, face landmarks, and agreed voice delivery features.
- The system extracts hand, gaze/head-pose, or equivalent visible communication features where technically practical.
- The system converts raw landmarks and audio features into assessment-ready signals.
- The system applies configurable local assessment logic.
Configuration changes can be hot reloaded without restarting the application.
- The system produces real-time feedback events. (BPS, body language, eye-tracking)
- The feedback events drive the agreed human-facing interface.
- The human-facing interface is separate from the assessment engine.
- The system includes a visual debug mode showing key extracted landmarks/features.
- Offline testing confirms no network calls (with exception of ollama cloud and initial system install), telemetry, analytics, or cloud dependencies.
- Source code is modular and documented.
- Handover package is complete.

ACCEPTANCE CRITERIA
- Live Demo
- Demonstrates webcam and microphone input being processed in real time.
- Shows extracted body/pose, face, and voice features.
- Shows real-time feedback being generated.
- Shows feedback driving the agreed human-facing interface.
- Configuration Demo
- Assessment logic is stored in local configuration files.
- A configuration value is changed during the demo.
- The system applies the changed logic without application restart.
- Offline Evidence
- Demo runs with network disabled.
- Logs or monitoring evidence show no external calls.
- No telemetry or analytics are present.
- Benchmark Evidence
- Benchmark report includes hardware used, model/runtime choices, FPS, latency, CPU/GPU usage, RAM usage, and sustained runtime.
- Any missed performance target is explained with evidence and mitigation options.
- Architecture Evidence
- Code review confirms separation between capture, feature extraction, assessment logic, configuration, feedback events, human interface, and application/UI layers.
- Interfaces between major components are documented.
- Architecture notes identify key trade-offs and known limitations.
- Source code, assets, configuration files, setup instructions, run commands, benchmark results, and known limitations are provided.
- Supplier confirms deletion of retained project materials unless written approval is given

PAYMENT CONDITIONS
* I’ll only accept work that meets the agreed standards. These will not change from what we've discussed

INVOICING
* Invoice like this please per week:
25hrs x 24.61 = 615GBP

Coding standards:
* Must have a clear and true seperation of concerns between FE and BE
* This means the front and backend are completely seperate and connected only by an integration layer.
* You can make superficial changes to the front layer, with zero risk to the backend.
* All files must be under 600 lines long inclusive of comments and single purpose. Only exception are shared utilities
* If a file exceeds 600 lines, then consider that it's either inefficient/bloated, or is doing more that one thing
* Examples of sinlge purpose: One file is for LLM integration, one file for feature x, another file for feature y (and so on)
* All code must include comments within files (applies only where possible, since you cannot add comments to some files due to syntax issues ). This includes:
* Before imports: file name with path + points on the unique features of the file
* Comments throughout code to explain what is dynamic such that any idiot like me can understand the code
* Ensure code is dynamic and flexible - not rigid, fragile or with hardcoded values .

We are seeking a Computer Vision / AI Pipeline Engineer for a vehicle-mounted field observation system. The role involves working with hardware, sensors, cameras, LiDAR, and GPS to develop and optimize AI pipelines. The ideal candidate will have experience in computer vision and AI engineering, with a strong understanding of mechanical engineering and microcontroller programming. This is a part-time role with a project scale of small, expected to last 1 to 3 months.

We are seeking an AI programmer to work on various AI projects. The ideal candidate will have experience in AI programming and be able to develop and implement AI solutions. Responsibilities include designing, developing, and testing AI systems, collaborating with cross-functional teams, and staying updated with the latest AI technologies.

We are looking for a remote Machine Learning Engineer to help build and deploy production document-AI systems. The work centres on optical character recognition (OCR), document analysis, computer vision, and NLP/LLM components that automate document-heavy operational processes. You will develop and optimise deep-learning models, build end-to-end ML pipelines (data ingestion, feature engineering, training, evaluation, inference), and deploy them using Docker, Kubernetes, and modern MLOps practices. Essential skills: strong Python; TensorFlow and/or PyTorch; OpenCV; and hands-on experience with OCR and computer vision. Familiarity with LLMs and document-analysis techniques (e.g., TrOCR) is a strong plus. This is an ongoing, fully remote contract role with flexible hours.

We are building a confidential next-generation AI media prototype that combines real-world visual recognition, multimodal content understanding, generative video creation, and spatial-style visual playback.

This is not a standard mobile app project. A smartphone or camera-equipped device may be used as the temporary capture and playback interface, but the core work is the AI pipeline: recognizing physical visual/text-based content, interpreting its meaning, generating a short AI-created visual sequence, and playing it back through a simplified reflective display/demo setup.

The detailed product concept will be shared only after NDA.

We are looking for an experienced engineer:

* Camera-based capture of physical visual/text content
* OCR and computer vision processing
* Multimodal AI interpretation of the captured content
* Structured JSON generation for scene, character, tone, action, and visual direction
* AI-generated image/video or animated visual sequence creation
* Playback mode optimized for a simple reflective display-style demo
* Lightweight backend/API pipeline for AI processing and asset storage
* A polished demo flow suitable for investor, partner, or internal product presentations

**Required Skills:**

* Computer vision and OCR
* Multimodal AI / LLM integration
* AI image generation and/or AI video generation
* Prompt engineering and structured JSON outputs
* Backend/API development
* Camera input and media processing
* Prototype UI or demo interface development
* Fast MVP execution under a 12-week timeline

**Nice to Have:**

* Experience with AR, spatial media, projection-style visuals, or reflective display demos
* Experience creating AI characters, animated scenes, or generative video workflows
* Unity, WebGL, React, Flutter, Swift, Kotlin, or similar demo-rendering experience
* Experience building confidential product prototypes or investor demo systems

**Confidentiality Requirements:**
The selected freelancer must sign an NDA, assign all work product/IP to the client, and keep the entire project fully confidential. The project, concept, screenshots, demos, prompts, workflows, code, and outputs may not be shared publicly or used in a portfolio without written permission.

**Application Requirements:**
Please share relevant examples of projects involving AI vision, OCR, multimodal AI, generative image/video, AR/spatial media, or interactive prototype systems. Screenshots, short videos, documents, or links are welcome. Please also describe what you would build in the first 2 weeks to prove technical feasibility.