Optimize Deep Learning Projects on Upwork

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed.

What we need:

Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame.
Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator.
Output: a single, smooth, watchable video stream (no visible seam/ghosting at the stitch line, no jittery panning).

What this is NOT: we are not looking for a deep-learning/ball-detection/player-identification system. Classical CV techniques (homography, background subtraction, centroid tracking, Kalman/low-pass smoothing) are the right toolset here — please don't propose a large AI/ML infrastructure buildout.

Ideal candidate:

Strong hands-on experience with OpenCV (stitching, homography, background subtraction)
Has shipped at least one real-time video processing project (not just offline/batch)
Comfortable working with two-camera / multi-camera rigs and frame synchronization
Bonus: experience with sports or surveillance camera systems

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed.

What we need:

Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame.
Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator.
Output: a single, smooth, watchable video stream (no visible seam/ghosting at the stitch line, no jittery panning).

What this is NOT: we are not looking for a deep-learning/ball-detection/player-identification system. Classical CV techniques (homography, background subtraction, centroid tracking, Kalman/low-pass smoothing) are the right toolset here — please don't propose a large AI/ML infrastructure buildout.

Ideal candidate:

Strong hands-on experience with OpenCV (stitching, homography, background subtraction)
Has shipped at least one real-time video processing project (not just offline/batch)
Comfortable working with two-camera / multi-camera rigs and frame synchronization
Bonus: experience with sports or surveillance camera systems

Skills: After Effects, C++ Programming, 3D Modelling, 3D Animation, OpenCV, Video Processing, Computer Vision, Video Streaming

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed.

What we need:

Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame.
Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator.
Output: a single, smooth, watchable video stream (no visible seam/ghosting at the stitch line, no jittery panning).

What this is NOT: we are not looking for a deep-learning/ball-detection/player-identification system. Classical CV techniques (homography, background subtraction, centroid tracking, Kalman/low-pass smoothing) are the right toolset here — please don't propose a large AI/ML infrastructure buildout.

Ideal candidate:

Strong hands-on experience with OpenCV (stitching, homography, background subtraction)
Has shipped at least one real-time video processing project (not just offline/batch)
Comfortable working with two-camera / multi-camera rigs and frame synchronization
Bonus: experience with sports or surveillance camera systems

Skills: After Effects, C++ Programming, 3D Modelling, 3D Animation, OpenCV, Video Processing, Computer Vision, Video Streaming

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed.

What we need:

Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame.
Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator.
Output: a single, smooth, watchable video stream (no visible seam/ghosting at the stitch line, no jittery panning).

What this is NOT: we are not looking for a deep-learning/ball-detection/player-identification system. Classical CV techniques (homography, background subtraction, centroid tracking, Kalman/low-pass smoothing) are the right toolset here — please don't propose a large AI/ML infrastructure buildout.

Ideal candidate:

Strong hands-on experience with OpenCV (stitching, homography, background subtraction)
Has shipped at least one real-time video processing project (not just offline/batch)
Comfortable working with two-camera / multi-camera rigs and frame synchronization
Bonus: experience with sports or surveillance camera systems

Skills: After Effects, C++ Programming, 3D Modelling, 3D Animation, OpenCV, Video Processing, Computer Vision, Video Streaming

We're building a system that works similarly to sports auto-tracking cameras (e.g. Veo), but with a simpler, classical computer-vision approach — no deep learning or trained models needed.

What we need:

Camera stitching: Combine footage from two fixed cameras (mounted on one rig, overlapping field of view) into a single panoramic image (~180°), using calibration/homography. The cameras don't move relative to each other, so this should be a one-time calibration applied per frame.
Motion-density tracking: From the panoramic feed, detect where players are concentrated (background subtraction / foreground blob density, not per-object classification) and use that to drive an automatic pan/crop — i.e. a virtual camera that follows the action without a human operator.
Output: a single, smooth, watchable video stream (no visible seam/ghosting at the stitch line, no jittery panning).

What this is NOT: we are not looking for a deep-learning/ball-detection/player-identification system. Classical CV techniques (homography, background subtraction, centroid tracking, Kalman/low-pass smoothing) are the right toolset here — please don't propose a large AI/ML infrastructure buildout.

Ideal candidate:

Strong hands-on experience with OpenCV (stitching, homography, background subtraction)
Has shipped at least one real-time video processing project (not just offline/batch)
Comfortable working with two-camera / multi-camera rigs and frame synchronization
Bonus: experience with sports or surveillance camera systems

Skills: After Effects, C++ Programming, 3D Modelling, 3D Animation, OpenCV, Video Processing, Computer Vision, Video Streaming

We are looking for an experienced Data Scientist to join our team and work on data-driven projects involving machine learning, predictive analytics, and advanced data analysis.

3+ years of professional experience in Data Science or a related field.

The ideal candidate should have strong experience working with Python, statistical analysis, machine learning models, and real-world datasets. You should be comfortable transforming raw data into meaningful insights and developing reliable predictive solutions.

Candidates must have strong English communication skills due to client facing work.

Key Responsibilities:

• Analyze and interpret complex datasets to identify meaningful patterns, trends, and insights.
• Clean, preprocess, and transform structured and unstructured data.
• Develop and evaluate machine learning and predictive models.
• Perform statistical analysis and exploratory data analysis.
• Build data-driven solutions using Python and relevant data science libraries.
• Develop classification, regression, clustering, and forecasting models when required.
• Perform feature engineering and model optimization.
• Create data visualizations, reports, and dashboards to communicate findings.
• Work with SQL databases and extract data for analysis.
• Evaluate model performance and improve accuracy, reliability, and scalability.
• Collaborate with developers, analysts, and project stakeholders.
• Document analysis, methodologies, models, and results clearly.

Requirements:

• 3+ years of professional experience in Data Science or a related field.
• Strong proficiency in Python.
• Strong understanding of machine learning concepts and algorithms.
• Experience with data analysis, statistical modeling, and predictive analytics.
• Experience with libraries such as Pandas, NumPy, Scikit-learn, and Matplotlib or similar tools.
• Strong SQL and database knowledge.
• Experience with data cleaning, preprocessing, feature engineering, and model evaluation.
• Strong analytical and problem-solving skills.
• Good English communication skills.
• Ability to work independently and collaborate effectively with a technical team.

Preferred Qualifications:

• Experience with TensorFlow or PyTorch.
• Experience with deep learning or Generative AI.
• Experience with NLP or computer vision.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with data visualization tools such as Power BI or Tableau.
• Experience working with large datasets and scalable data pipelines.

If you have strong experience building practical machine learning and data science solutions, please share relevant projects, GitHub repositories, portfolio links, or examples of your previous work with your proposal.

About the Role
We're looking for a Data Scientist with a strong foundation in Machine Learning, Computer Vision, and Image Processing who enjoys solving real-world problems from start to finish.
This is a hands-on role that goes beyond building models. Depending on the project, you'll be involved in understanding customer requirements, designing technical solutions, preparing datasets, training and evaluating models, deploying solutions, and collaborating with both technical and non-technical stakeholders.
We're a small, fast-moving team, so every team member has a meaningful impact. We're looking for someone who takes ownership, thinks independently, and is comfortable driving tasks forward with minimal supervision.

What You'll Do
-Designing and developing Machine Learning and Computer Vision solutions.
-Preparing, organizing, and managing datasets for training and evaluation.
-Training, testing, and improving Machine Learning models.
-Deploying models into production environments.
-Monitoring model performance and identifying opportunities for improvement.
-Investigating technical challenges and proposing practical solutions.
-Understanding customer requirements and translating them into technical implementations.
-Participating in technical discussions with customers and internal team members when needed.
-Creating clear documentation for datasets, models, workflows, and project decisions.
-Collaborating with developers, customers, and other stakeholders throughout the project lifecycle.
-Continuously improving workflows, processes, and solution quality.

Required Skills
-Strong Python programming skills.
-Basic SQL knowledge.
-Good understanding of Machine Learning fundamentals.
-Experience or strong interest in Computer Vision and Image Processing.
-Experience working with common ML frameworks such as PyTorch, TensorFlow, or similar.
-Strong analytical and problem-solving skills.
-Excellent written and verbal English communication skills.
-Ability to explain technical concepts clearly to both technical and non-technical audiences.
-Strong organizational and documentation skills.
-Ability to work independently and manage multiple tasks effectively.

What We're Looking For
We're not simply looking for someone who can complete assigned tasks—we're looking for someone who takes ownership.
The ideal candidate:
-Takes responsibility for delivering solutions, not just writing code.
-Thinks critically and independently.
-Identifies problems before they're pointed out.
-Proactively asks questions when requirements are unclear.
-Looks for solutions instead of waiting for instructions.
-Is comfortable making technical decisions and justifying them.
-Communicates clearly and keeps stakeholders informed.
-Is curious, adaptable, and eager to learn.
-Takes pride in producing high-quality work.

Working Hours
-We start at approximately 30 hours per week
-Flexible working hours.

Project Duration
-Initial paid trial period of 1 month
-Long-term collaboration based on performance.

Why Join Us?
-Work on challenging AI and Computer Vision projects with real-world impact.
-Be involved in the entire project lifecycle—from idea to deployment.
-High level of ownership and autonomy.
-Flexible working environment.
-Opportunity for long-term collaboration and professional growth.
-Small team where your ideas and contributions genuinely matter.

How to Apply
Instead of sending a generic application or simply summarizing your work history, we'd like to understand how you think, solve problems, and what you can contribute to our team.

Please submit a cover letter that tells us:
-What Computer Vision, Image Processing, or Machine Learning projects you've worked on and your specific contributions.
-What technical challenges you've solved and what responsibilities you personally owned.
-A project you're particularly proud of and why.
-How you approach unfamiliar problems or situations where requirements are incomplete.
-What you believe you would bring to our team.
-Why you believe you're the right fit for this role.
-We place a high value on ownership, initiative, and independent thinking.
We're looking for people who enjoy solving problems, taking responsibility, asking thoughtful questions, and continuously improving both themselves and the projects they work on.

If you're someone who likes being trusted with meaningful responsibility and making a real impact, we'd love to hear from you.

I want to guide a small group of absolute beginners all the way to the point where they can land an entry-level role in AI, machine learning and generative AI. To get there, I’ll need a mentor who can design and deliver a complete learning journey—covering core theory just enough to ground them, then pivoting quickly into hands-on coding, model training and project work that showcases real-world skills.

Here’s what I’m expecting from you:
• A clear, modular curriculum that progresses from Python basics through classic machine-learning workflows and on to modern generative models (GPT-style LLMs, diffusion, etc.).
• Live or recorded sessions with practical demos, Jupyter notebooks and datasets my trainees can keep experimenting with after class.
• Capstone projects substantial enough to populate a portfolio and demonstrate job-ready competence; guidance on refining those projects for GitHub and technical interviews.
• Ongoing Q&A support—Slack, Discord or similar—and lightweight progress checkpoints so nobody falls behind.
• Suggestions for open-source contributions, Kaggle competitions or hackathons that fit naturally into the timeline.

I’m flexible on tools as long as they are industry-relevant—TensorFlow, PyTorch, Hugging Face, LangChain, Google Colab or local GPU setups are all fine. Please outline how many weeks you’d need, the approximate hours of live contact, and what deliverables (slides, notebooks, code repos) you’ll hand over at each milestone.

Skills: Java, Python, Statistics, Machine Learning (ML), Deep Learning, Generative AI, Hugging Face, LangChain

An end-to-end system that turns the day's AI news into finished YouTube videos (roundups + deep-dives) with almost no human touch — ingesting sources, clustering stories, writing a retention-tuned script, voicing it, and rendering the visuals.

It runs a cost-optimized mix of LLMs (gpt-4o-mini for structural planning, DeepSeek for creative writing) with self-healing fallbacks, a Gemini production voice, and a motion-graphics-as-code render engine, all driven by a comedic, meme-forward "AI host by Eswar" editorial voice.

The newest workstream adds emotion-first B-roll: a beat-level shot planner + a provider-agnostic asset-discovery module (Pexels/Pixabay + vision-verification + FLUX/local generation) that builds a reusable, metadata-rich visual asset library.

MERLx (MERLx.org) is a small Spain-based analytics studio building AI-native tools for monitoring, evaluation, research and learning (MERL) in the development and humanitarian sector. Our users are M&E and programme teams at NGOs, UN agencies and donors who need to make sense of messy field data, evidence and reporting quickly, and who need to be able to trust what the tools tell them.

I am looking for a lead engineer to take ownership of the technical side of the product suite: PRISM, IRIS, Simulation Lab, Rapport and a Theory of Change tester, alongside a learning platform for MERL practitioners. I lead product, domain and design direction. You would own architecture, build and delivery.

HOW THIS ENGAGEMENT WORKS
This post is deliberately scoped as a short first engagement of under a month so we can test the fit before committing to something longer. In that window I would expect you to get across the current codebase and architecture, agree a technical roadmap with me, and ship one well-defined feature or component end to end. If it works for both of us, the intent is an ongoing lead role across the whole suite.

WHAT YOU WOULD WORK ON
- Python backends with LLM pipelines, RAG and agent workflows, built so that outputs are traceable and sector-specific facts are flagged for human verification rather than generated with false confidence
- React/TypeScript front-ends for data-heavy interfaces, implemented against an existing token-based design system
- AWS deployment and data pipelines, kept simple and cost-aware for a small studio
- Data and geospatial work with public sector datasets such as HDX, ACLED, DHS and Earth Engine, feeding analytics, simulations and dashboards

WHAT I AM LOOKING FOR
- You have shipped production LLM/RAG systems, not just prototypes, and you know where they break
- Strong Python and solid TypeScript/React, comfortable owning both ends of the stack
- You can make architecture decisions and explain them plainly to a product owner who is a data scientist, not a software engineer
- You work well asynchronously with a small team, communicate clearly in writing and do not need much hand-holding
- Interest in the humanitarian and development sector is a plus. Domain knowledge is not required; I will bring that.

TO APPLY
Tell me briefly about one LLM or data product you took from idea to production, what you would do differently now, and your availability over the next month.

GENAI & AGENTIC AI EXPERT / MENTOR REQUIRED

We are looking for an experienced Generative AI & Agentic AI Engineer/Mentor for daily 1-hour online mentoring sessions.

The training is focused on practical, hands-on learning rather than theory. The mentor should have strong real-world and live/production project experience in Generative AI and Agentic AI.

SCHEDULE

• 1 hour daily
• 12:00 PM – 1:00 PM IST OR
• 4:30 PM – 5:30 PM IST
• Monday to Friday

CURRICULUM

The complete course curriculum, including all sessions and topics, is provided in the attached Excel sheet.

Please review the attached Excel carefully before applying and confirm that you are comfortable covering the complete curriculum.

WHAT WE EXPECT

• Live coding and practical implementation
• Real-world project-based learning
• Architecture and system design discussions
• Debugging and problem-solving
• Hands-on assignments and tasks
• Production-level best practices
• Guidance on building real GenAI and Agentic AI projects
• Capstone project guidance

REQUIRED EXPERIENCE

We are looking for someone with strong hands-on experience in:

• Python
• Generative AI / LLMs
• RAG and Vector Databases
• LangChain / LangGraph
• Agentic AI and Multi-Agent Systems
• CrewAI / AutoGen
• Prompt Engineering
• LLM Fine-Tuning / LoRA / QLoRA
• Hugging Face
• FastAPI / APIs / WebSockets
• Docker and Cloud Deployment
• LLM Evaluation and Observability
• MCP

IMPORTANT

Candidates with actual live/production project experience will be strongly preferred.

We are NOT looking for someone who has only theoretical knowledge or teaching experience. We need a mentor who has actually built and worked on real-world GenAI/Agentic AI applications.

TO APPLY

Please provide:

1. Total experience in GenAI / Agentic AI
2. 2–3 real-world projects you have worked on
3. Your experience with LangChain / LangGraph / CrewAI / AutoGen
4. Your experience with RAG and Multi-Agent Systems
5. GitHub / Portfolio / LinkedIn
6. Your hourly rate
7. Your availability
8. Preferred time slot: 12–1 PM IST or 4:30–5:30 PM IST
9. Confirmation that you have reviewed the attached curriculum Excel and can cover the complete course

We are looking for a long-term mentor who can provide practical, industry-oriented guidance and help develop strong hands-on expertise in Generative AI and Agentic AI.

I need a custom real-time signal transmission system featuring a sender application and a receiver admin panel. The sender app should have a minimalist black interface and allow me to transmit instant signals using the mobile's volume up and down buttons while streaming a live match. These volume actions should also provide haptic feedback. All signals must be captured in real-time by the receiver admin panel with ultra-low latency.

also gave you an attach sender application file sample..

Project Description:
Developed an end-to-end AI-powered medical intelligence platform for analyzing chest X-ray images and assisting with disease prediction. The system uses Deep Learning with DenseNet121 to classify X-ray images into five categories: Normal, Pneumonia, Atelectasis, Cardiomegaly, and Effusion. Integrated Grad-CAM Explainable AI (XAI) to generate visual heatmaps highlighting regions of the X-ray that contributed to the model's prediction. Built REST APIs using FastAPI for image upload, prediction, model information, and health monitoring. The platform also supports prediction history and is designed for integration with AI-assisted medical report generation using LLMs.

Key Features
1. Medical Image Analysis – Processes chest X-ray images using deep learning.
2. Disease Classification – DenseNet121-based classification for 5 medical categories.
3. Explainable AI – Grad-CAM generates heatmaps to visualize important image regions.
4. FastAPI REST API – Provides scalable endpoints for image prediction and model services.
5. Prediction Results – Returns predicted condition and confidence score.
6. AI-Assisted Reporting – Designed to generate structured medical reports using LLM integration.
7. Prediction History – Supports storing prediction information, model version, confidence, and report details.
8. Deployment Ready – Structured for Docker/containerized deployment.
9. Validation & Safety – Includes input validation and medical-use disclaimers.

Skills: Data Processing, Docker, Computer Vision, Deep Learning, FastAPI, REST API, Large Language Model, AI Model Integration

Remote Sensing + AI/ML Model for Identification of 10 Major NTFP Species in Jharkhand, India

We are looking for an experienced "Remote Sensing / Computer Vision / Geospatial AI developer" to develop or fine-tune an AI/ML model capable of identifying and mapping "10 major Non-Timber Forest Product (NTFP) tree species in Jharkhand, India" using remote sensing imagery.

Target Species

1. Sal
2. Mahua
3. Kusum
4. Tamarind
5. Kendu
6. Palash
7. Chironji
8. Amla
9. Harra
10. Bahera

Objective

The objective is to develop a reliable workflow that can identify these species from remote sensing data and ultimately generate a **species-wise tree inventory/map**, including tree locations and counts.

Scope of Work

We are open to either:

Developing a new model from scratch or
Fine-tuning/adapting an appropriate open-source model such as DeepForest or other tree detection/segmentation and species-classification frameworks.

The expected workflow may include:

Remote Sensing Imagery → Individual Tree/Crown Detection → Feature Extraction → Species Classification → GIS Species Map & Tree Count

Potential data sources may include:

* High-resolution satellite imagery
* Multispectral imagery
* Sentinel-2 time-series data
* Drone imagery, where required
* Ground-truth/GPS data from field surveys

The model should ideally make use of **spectral, temporal, textural and/or crown structural features** where appropriate.

Expected Deliverables

* Working AI/ML model and complete source code
* Pre-processing and training pipeline
* Individual tree/crown detection or segmentation
* Classification of the 10 target species
* Species-wise tree count
* GIS-ready output (Shapefile/GeoJSON/GeoPackage or equivalent)
* Confidence/probability score for each prediction
* Accuracy assessment and confusion matrix
* Documentation explaining the methodology, training process and inference workflow
* Recommendations for scaling the model to larger areas of Jharkhand

Ideal Candidate

Please apply if you have demonstrated experience in:

* Remote sensing and satellite image processing
* Computer vision / deep learning
* Tree detection and crown segmentation
* Tree species classification
* GIS / GeoPandas / Rasterio / Google Earth Engine
* Python, PyTorch/TensorFlow
* Models such as DeepForest, YOLO, Mask R-CNN, U-Net, SAM or similar
* Multispectral/hyperspectral imagery
* Geospatial AI

Experience with forest/tree species mapping is highly preferred.

Skills: Python, Machine Learning (ML), Remote Sensing, Image Processing, Computer Vision, Deep Learning, YOLO

We are an early-stage AgTech startup developing an AI-driven system designed to better understand plant behavior, growth, and stress.

We are looking for an experienced AI / Machine Learning Engineer to help us develop the first stage of our AI technology.

The role will involve working closely with our team and agricultural experts to evaluate existing data, determine what can realistically be achieved with it, and develop an initial predictive AI model.

What we're looking for
Strong machine learning / deep learning experience
Python
Predictive modeling
Time-series modeling
Experience building and validating models using real-world data
PyTorch, TensorFlow, or similar frameworks
Strong analytical and problem-solving skills

Experience in AgTech, plant science, biological modeling, environmental systems, or similar fields is a significant advantage.

We are looking for someone who can contribute not only as a developer, but also help us determine the right technical approach and realistic capabilities of the model.

This is an initial project with the potential to develop into a long-term collaboration as the technology progresses.

When applying, please include examples of relevant predictive AI/ML systems you have previously developed.

Project type: Fixed-price freelance project
Duration Approximately 4–6 weeks, part-time
Indicative budget $1,800–$2,500 depending on experience and proposed approach
Stack Python, OpenCV, NumPy, PyTorch/
appropriate, REST API

We are developing a computer-vision system for the analysis of
medical images and are looking for a freelance Computer Vision / Edge ML Engineer to develop a lightweight Image Quality Assessment (IQA) module

What the module should assess

The system should identify quality issues including but not limited to:
Image blur / lack of focus, Under-exposure and over-exposure, Excessive glare / reflections,Poor etc.

The module should return an interpretable PASS/FAIL decision together with the reason(s) for rejection and, where useful, individual quality scores.

Key technical constraint (important) - *Low computational cost is a primary requirement.*
The solution should run efficiently on an average CPU and should preferably be suitable for eventual mobile/edge deployment.

We are particularly interested in candidates who can determine when classical computer-vision techniques are sufficient and when a small learned model is justified. Solutions dependent on large deep-learning models, GPUs or cloud-based model inference are not appropriate for this project.

Potential approaches may include OpenCV-based image statistics, sharpness/frequency measures, histogram and illumination analysis, etc.

Expected work and deliverables
1. Review representative labelled medical image data and characterize common quality failures.
2. Benchmark candidate approaches for each quality criterion.
3. Develop the image-quality assessment pipeline.
4. Determine and validate appropriate thresholds/classification criteria.
5. Optimize the implementation for CPU/edge execution.
6. Evaluate performance on a held-out dataset.
7. Benchmark inference latency, memory consumption and model size.
8. Deliver clean, maintainable Python code with appropriate tests andtechnical documentation.
9. Package the completed IQA module as a deployable
API/microservice that can be integrated into an existing application architecture.

The API should:
Accept an image as input.
Run the image-quality assessment.
Return a structured response containing PASS/FAIL status, identified quality problems and relevant quality scores.
Include appropriate error handling and input validation.
Be documented sufficiently for integration by another developer.
Be containerized (preferably Docker) so that it can be deployed as an independent microservice.
Run without requiring GPU infrastructure.

Desired experience
* Strong Python and OpenCV experience
* Practical computer vision/image-processing experience
* Understanding of image sharpness, exposure, illumination and image-quality metrics * Experience building computationally efficient CV pipelines
* PyTorch experience
* Experience building REST APIs in Python (FastAPI preferred)
* Docker/containerization experience
* ONNX, TensorFlow Lite or mobile/edge ML experience is highly desirable

Medical-imaging experience is useful but **not required**.

### Definition of successful delivery

At completion, we expect to receive:

* A validated image-quality assessment pipeline
* Performance results on a held-out test dataset
* Per-quality-criterion evaluation metrics
* CPU inference-time and resource benchmarks
* Source code and configuration
* Trained model weights, if learned components are used
* Tests and technical documentation
* A documented, containerized REST API that can be deployed as an
independent microservice
* Instructions for running the service locally and deploying it into
an existing application environment

All source code, trained models and project-specific implementation
produced under the engagement must be delivered as part of the
project.

### When applying

Please provide:

1. 1–3 examples of relevant computer-vision projects you personally implemented.
2. Your experience with OpenCV and lightweight/edge ML.
3. Your experience deploying ML/CV models as APIs or microservices.
4. A brief description of how you would approach blur, exposure and
incorrect-view detection while minimizing computational requirements.
5. Your proposed timeline.
6. Your fixed-price quotation.

Shortlisted candidates may initially be offered a **small paid
technical milestone/proof-of-concept** before proceeding with the
complete project.

Please do not submit generic AI/LLM portfolios. We are specifically looking for hands-on computer vision, image processing and lightweight ML engineering experience.

I need someone to jump on AnyDesk or TeamViewer and turn my Windows 11 desktop with an NVIDIA GPU into a fully portable DeepFaceLive box. Start by checking which CUDA toolkit is already present—I honestly don’t know—then install or upgrade whatever version best matches the GPU and DeepFaceLive’s current build.

Once CUDA is sorted, set up a clean, self-contained (no system-wide edits) Python environment; I’m flexible on the exact 3.x version as long as every required library plays nicely with the GPU. Finish by enabling the virtual camera so I can push the swapped feed into Zoom, OBS or Discord without additional tweaks.

Acceptance test
• Launch DeepFaceLive from the portable folder you create
• Load a sample model and run a live face-swap feed from my webcam
• Achieve and hold 30 FPS or better with no perceptible latency
• Show the virtual camera being recognised by a conferencing app during the session

I’ll sign off only after seeing that live demo run smoothly end-to-end.

Skills: PHP, Python, Software Architecture, CUDA, C++ Programming, Deep Learning, AI Model Development, AI Video

We’re exploring a Python-based tool for automatically extracting individual building and tent footprints from very-high-resolution satellite imagery.

We’re looking for an experienced consultant to review the requirements and recommend a practical technical approach.

Technical context
- Satellite imagery: Pléiades Neo at 30 cm, SkySat and Pléiades at 50 cm.
- Existing imagery and manually validated footprints potentially available for training, subject to a data-quality assessment.
- Outputs: individual object polygons and counts, exportable as GeoJSON or shapefiles and compatible with ArcGIS.
- Intended deployment: AWS g5.4xlarge with an NVIDIA A10G GPU, currently Windows-based. Linux is an option if justified.
- Target processing volume: approximately 100 km² within a day.

No model or architecture has been selected. We’re interested in an independent assessment grounded in practical experience.

Relevant experience:
- Hands-on experience with building or shelter extraction from satellite or aerial imagery, training and fine-tuning segmentation models, Python-based deep learning, geospatial data processing and deployment.
- Experience producing usable GIS polygons from model predictions is particularly relevant.

Please include a brief description of relevant projects, your contribution, availability and your rate for an initial consultation.

# AI/ML Engineer – Generative AI, LLMs & AI Agents

We’re looking for an experienced **AI/ML Engineer** to help us design, build, and deploy production-ready AI solutions. The ideal candidate has strong hands-on experience with **Generative AI, LLMs, RAG, AI Agents, NLP, Python, and machine learning**, along with solid software engineering skills.

This is a hands-on engineering role where you’ll work on real AI products and help turn ideas and prototypes into reliable, scalable applications.

## What You’ll Work On

* Design and develop **AI/ML and Generative AI applications**
* Build **LLM-powered applications, AI agents, and intelligent automation workflows**
* Develop and optimize **RAG (Retrieval-Augmented Generation)** pipelines
* Work with LLM APIs and models such as OpenAI and other modern foundation models
* Implement document processing, embeddings, vector search, semantic search, and knowledge bases
* Develop AI agents capable of using tools, APIs, databases, and external services
* Build and integrate AI functionality into existing web/mobile applications
* Develop and improve machine learning/NLP pipelines when required
* Evaluate model performance, accuracy, latency, and cost
* Deploy AI solutions to cloud environments and production systems
* Write clean, maintainable, well-tested Python code
* Collaborate with product and engineering teams to define technical solutions

## Required Skills

**Core:**

* Python
* Machine Learning
* Deep Learning
* NLP
* Generative AI
* Large Language Models (LLMs)
* RAG
* Prompt Engineering
* AI Agents / Agentic AI
* API development and integration
* Git and software engineering best practices

**AI/LLM Ecosystem:**

* OpenAI APIs
* LLM application development
* Embeddings and vector databases
* LangChain, LangGraph, LlamaIndex, or similar frameworks
* Vector search and semantic search
* Model evaluation and optimization

**Backend & Cloud:**

* FastAPI or similar Python frameworks
* REST APIs
* SQL/NoSQL databases
* Docker
* AWS, Azure, or GCP
* Production deployment and monitoring

## Nice to Have

* Experience fine-tuning or adapting LLMs
* Experience with open-source models such as Llama, Mistral, or similar
* Computer vision experience
* Experience building multi-agent systems
* AI workflow automation
* Experience with PostgreSQL, Redis, or vector databases such as Pinecone, Weaviate, Qdrant, or pgvector
* Kubernetes or other cloud-native technologies
* Experience building AI SaaS products or MVPs

## What We’re Looking For

We’re not looking for someone who has only experimented with ChatGPT or basic AI APIs.

We need an engineer who understands **how to build production-grade AI systems** — from architecture and model selection to implementation, integration, deployment, monitoring, and optimization.

You should be comfortable taking an ambiguous problem, proposing a practical technical approach, and independently delivering working solutions.

## To Apply

Please include:

1. A brief introduction about your AI/ML experience.
2. 2–3 relevant projects involving **LLMs, RAG, AI Agents, or Generative AI**.
3. The technologies and frameworks you used.
4. Your experience deploying AI applications to production.
5. Links to GitHub, portfolio, or relevant work if available.
6. Your hourly rate and availability.

**Bonus:** Start your proposal by describing how you would approach building a production-ready LLM/RAG or AI-agent system.

I'm looking for a senior developer with hands-on experience defeating Arkose Labs FunCaptcha (specifically the newer game variants such as hopscotch_highsec / hopscotchv3) in a headless / API-based pipeline.
▎
▎ I already have a working Node.js codebase that:
▎ - Mints Arkose tokens via a real-Chrome TLS client (bogdanfinn)
▎ - Rotates residential proxies + BDA fingerprints per attempt
▎ - Uses commercial classifiers (YesCaptcha, OmoCaptcha, rosolve.pro)
▎
▎ The pipeline reaches the challenge-submit step consistently, but the classifiers we use are giving low-accuracy answers on the newer navigation-style variants, so we're getting solved:false on the final wave.
▎
▎ What I need help with:
▎ - Diagnosing whether the failure is truly answer-quality vs a deeper trust/suppression issue
▎ - Improving classifier accuracy on the current variants (open-source model, custom ONNX, or an alternative provider you know works)
▎ - Sanity-check the request shape / fingerprint completeness against what current bypass techniques require
▎
▎ This is for personal research / private use. I have an existing codebase I can share privately in DM. Please share your experience with similar challenges — write "Arkose" at the top of your reply so I know it's not a template response

Skills: Python, Machine Learning (ML), Node.js, Data Science, Neural Networks, Computer Vision, Deep Learning, API Development

We're a very young legal-tech AI company (Estonian OU) building an analysis platform for legal document review. Several backend scoring and classification functions currently return placeholder or hardcoded outputs and need to be replaced with working logic based on real analysis of input text. The work includes implementing dynamic scoring and classification using LLM API calls and/or rule-based NLP, improving accuracy, and ensuring reliable output for legal document review workflows.

We are seeking an experienced AI and Machine Learning specialist to work on innovative projects involving deep learning and data science. The ideal candidate will develop advanced models, analyze complex datasets, and contribute to the design of intelligent systems. You will collaborate with cross-functional teams to deliver impactful solutions that leverage AI technologies. If you have a strong background in machine learning frameworks and data analysis, we want to hear from you!

I’m ready to collaborate with a stateside programmer who speaks and writes English effortlessly and located in only US. The work revolves around building and refining products that lean heavily on Python, JavaScript, Java, and broader Artificial Intelligence concepts. Day to day you’ll be hands-on with Machine Learning pipelines, Natural Language Processing services, Computer Vision modules, and Retrieval-Augmented Generation (RAG) techniques, transforming ideas or research into clean, production-ready code.

We will outline milestones together, but every sprint will share the same expectations: write maintainable code, test it thoroughly, push through Git, and discuss design decisions in real time during U.S. business hours. If you can point to recent projects that showcase deep ML, NLP, CV, or RAG expertise—and you enjoy rapid iteration—you’ll fit right in.

When you reply, include a short note on your most complex AI build, a link to code or demos highlighting your Python, JavaScript, or Java skills, and your typical weekly availability. I’m set to start as soon as the right partner steps forward.

Skills: Java, JavaScript, Python, Training, Software Architecture, Machine Learning (ML), Computer Vision, Deep Learning, Natural Language Processing, Retrieval-Augmented Generation (RAG)

Self-Hosted Facial Recognition API – Python / PHP

We need a developer to build a **self-hosted facial recognition system** using available open-source/pre-trained libraries.

We do **not** want to use paid services such as Azure Face API, AWS Recognition, Face++ etc.

The system should run on our own server and provide a simple **web service/API** which our existing PHP application can call.

Main requirements:

* Enrol a person using one or more face images
* Preferably work even when only **one enrolment image** is available
* Recognise an enrolled person from a submitted image
* Return Person ID / match result / confidence score
* Ideally support multiple faces in one image
* Store facial embeddings/templates efficiently
* Fast response and suitable for many enrolled users
* Simple REST/API interface using POST requests
* Provide PHP example code for sending images and receiving JSON response
* Full source code and installation instructions

You may use suitable technologies such as Python, OpenCV, ONNX, InsightFace/ArcFace, FAISS or other appropriate open-source solutions.

We are **not asking to create an AI model completely from scratch**. Using and configuring suitable existing pretrained models/libraries is acceptable.

The final solution must operate independently on our server without any mandatory per-image or monthly facial-recognition subscription.

When bidding, please briefly mention:

1. Which facial recognition library/model you propose.
2. Whether you have done similar work.
3. Whether it can work with single-photo enrolment.
4. Approximate development time and cost.

We are a health-tech startup exploring whethr wearable sensor data can reliably distinguish stress states from baseline in real-world conditions. Before committing to a full pipeline build, we want a proof-of-concept notebook that shows feasibility using a public dataset.

Scope:

Use an open-source wearable dataset (eg. WESAD or similar) containing PPG, accelerometer, and physiological signals with labeled stress/baseline segments
Preprocess raw sensor streams: filtering, artifact rejection, segmentation into usable windows
Extract time-domain and frequency-domain features from the physiological signals
Train and evaluate at least one classification model for stress vs. baseline, using proper per-subject cross-validation
Provide a brief analysis of which features contribute most to the classification
Deliver one clean, documented Jupyter notebook that we can run and extend

Required:

Signal processing experience (filtering, spectral analysis, feature extraction from noisy sensor data)
ML classification on time-series data (Python, scikit-learn, PyTorch or TensorFlow)
Familiarity with proper evaluation methodology for subject-level physiological data

Preferred:

Background in electrical engineering or biomedical engineering
Experience with wearable sensor data or physiological signals
Familiarity with explainability methods

I'm looking for a skilled developer to create both an agent-based simulation and an AGI system.

Requirements:
- Develop a comprehensive agent-based simulation.
- Design and implement AGI with advanced cognitive abilities.
- Integrate both systems for seamless interaction.

Ideal Skills & Experience:
- Expertise in AI and simulation technologies.
- Proven experience in AGI development.
- Strong programming skills in relevant languages.


Please submit detailed project proposals.

Skills: Machine Learning (ML), Deep Learning, Machine Learning Algorithms, AI Model Development, AI Research, AI Development, AI Agents, AI Integration, AI Automation, AI Strategy

I have a time-series collection of remote sensing data—specifically multi-spectral satellite imagery—and I need an AI model that can reliably pinpoint and quantify changes that occur from one acquisition date to the next. The end goal is an automated workflow that flags where and when significant alterations appear, whether those are shifts in vegetation, newly built structures, or other surface transformations.

To give you an idea of scope, the raw scenes are already orthorectified and radiometrically corrected. What I’m missing is the machine-learning layer that will take two (or more) aligned images, learn the patterns of normal variability, and then output clear, georeferenced change masks and summary statistics.

Key expectations
• Model architecture, training pipeline, and inference script packaged in Python (TensorFlow, PyTorch, or another proven deep-learning framework).
• Clear instructions for reproducing results on my own machine, including environment file and command-line steps.
• Evaluation report showing accuracy metrics on a held-out test set I will provide after initial proof of concept.

If you have experience with satellite imagery, convolutional networks, and change detection techniques such as Siamese or UNet-based approaches, I’d like to see examples of similar work in your bid.

Skills: Python, Data Processing, Software Architecture, Machine Learning (ML), Remote Sensing, Deep Learning, Predictive Analytics, Time Series Forecasting, Time Series Analysis, Model Evaluation

AdhiAntha

Ganesh Utsav Invitation
|| श्री गणेशाय नमः ||
AdhiAntha
We warmly invite you and your family to join us in welcoming Ganpati Bappa into our home for Ganesh Utsav 2026!
Your presence will add immense joy and warmth to our celebrations as we seek Bappa's blessings together.
Event Schedule
Monday, September 14, 2026
4:30 PM – Ganesh Sthapna
Tuesday, September 15, 2026
8:00 PM – 11 Hanuman Chalisa Recitations
Thursday, September 17, 2026
8:00 PM – Annakut (56 Bhog) & Maha Aarti
Friday, September 18, 2026
9:30 PM – Visarjan Pooja
Daily Aarti Timings
Morning: 8:00 AM
Evening: 7:30 PM
The Significance of AdhiAntha
Adhi (Adi): Means "The Beginning," representing Lord Ganesha, who is worshipped first before any new venture or ritual.

Antha (Anta): Means "The Eternal," representing Lord Hanuman, the Chiranjeevi (immortal) destined to protect and live through eternity.


Venue & Contact Details
Address: 23 Chancellor Drive, Scarborough, ON M1G 2W4
Contact: 647-687-3561
With Warm Regards & Best Wishes,
Karm Patel | Aman Solanki | Drashti Patel | Arpita Makwana | Karmesh Patel
Jaykishan Raithatha | Rudra Patel | Ansh Patel | Yashna Patel | Kevin Patel
|| AdhiAntha Prabhu ||


Put one song on video I will post picture of it

Skills: Graphic Design, Video Services, Animation, After Effects, Poster Design, Videography, Video Editing, Adobe Premiere Pro, Deep Learning, Video Post-editing

We are looking for a skilled AI Image Generator to create high-quality visuals based on creative concepts, references, and project requirements. The ideal candidate should be able to turn ideas into polished and visually appealing AI-generated images.

Responsibilities:
Generate images based on provided concepts and references
Create different visual styles and compositions
Refine prompts to achieve the desired results
Maintain quality and consistency across images
Make basic edits and improvements when needed

We're a UK media & IP company building an internal decision-intelligence platform that analyses creator audiences at scale — engagement patterns, cross-platform presence, fandom behaviour — to support investment decisions in creator-led IP. The commercial thesis is confidential; the engineering is described fully below and an NDA covers the rest after hire.

We're looking for a hands-on ML/data engineer to build the platform end to end as a contractor, working directly with the founder and our Head of IP Intelligence. The stack and architecture are already specified in a detailed PRD with numbered requirements and acceptance criteria — you'll be building to a real spec, not guessing at scope.

What you'll build (four workstreams):

Collection pipelines — YouTube Data API connector (channels, video stats time-series, comment corpora) with quota management and audit logging; a video transcription pipeline (self-hosted Whisper or managed API — your recommendation); scheduled public-data snapshots (cross-platform follower counts, Google Trends, Wikipedia pageviews); all landing in BigQuery.
Feature engineering — a versioned signal library in dbt: engagement ratios benchmarked against size-matched cohorts, LLM-based comment classification at corpus scale (batch API, pinned prompt/model versions), transcript-based content analysis, and point-in-time correct features (every feature computable "as of" an arbitrary historical date — this is a hard requirement, it powers our backtesting).
Scoring & backtesting — a calibrated gradient-boosted scoring model (XGBoost or LightGBM) over the engineered features with confidence intervals and driver attribution; an evaluation harness that tests the score against baseline heuristics on a historical corpus (AUC, precision-at-k); LLM-generated evidence reports from templates; per-run cost tracking with hard monthly spend caps.
Review UI & decision log — an internal tool (Retool or lightweight web app, your call) showing scores, evidence, comparator cohorts and time-series per candidate, with a decision-capture form writing an immutable, append-only decision log to the warehouse.

Engagement shape: ~60–80 working days across 6–9 months, front-loaded (roughly full-time for the first 6–8 weeks, then 2–3 days/week). Fully remote; we're UK-based, so at least 3–4 hours of overlap with UK working hours. Start within 2–3 weeks.

How we work: everything in company-owned GitHub and GCP from day one; full IP assignment on payment (non-negotiable); CI with dbt tests on every model; documentation is a deliverable, not an afterthought — the acceptance bar is that a second engineer can navigate the platform cold. Milestone-based acceptance against the PRD checklist.

You're a fit if you have:

3+ years building data/ML pipelines in Python with a cloud warehouse (BigQuery strongly preferred) and dbt in production
Real experience with LLM APIs in batch analytical workflows (classification, extraction, structured output) — including prompt versioning and cost control, not just chat integrations
Classical ML depth: gradient boosting, calibration, honest evaluation methodology (you can explain why precision-at-k matters more than accuracy here)
API integration craft: pagination, rate limits, backoff, idempotency — you've fought the YouTube Data API or similar and won
Evidence of documentation quality (a repo, dbt docs site, or technical writing you can share)

Nice to have: point-in-time/as-of feature engineering (feature stores, backtesting systems, quant background welcome); scraping platforms (Apify or similar); Retool; social/creator/audience data experience.

Not a fit: deep-learning research profiles (there's no model training beyond classical ML here), pure BI/dashboard specialists, or agencies proposing teams — we want one accountable individual.

To apply, answer the screening questions below. We'll shortlist within a week, hold one 60-minute technical conversation (walking through your past work and our architecture — no take-home test), check one reference, and start on a paid pilot milestone.

I need a computer-vision model that reliably spots people in an image or live video feed, isolates their faces, and recognises each face while also labelling the dominant emotion it displays (happy, sad, neutral, etc.). My priority is accuracy in varied lighting and camera angles, with real-time or near-real-time inference on a mid-range GPU or edge device.

You may work in Python with libraries such as PyTorch or TensorFlow—use whichever stack lets you achieve strong precision and recall without excessive latency. A well-curated public dataset is fine for the initial build, but please leave hooks so I can fine-tune later with my own images.

Deliverables
• Trained face-recognition + emotion-detection model (weights + architecture)
• Inference script or REST/GRPC API that accepts images/streams and returns bounding box, ID, and emotion label
• Brief report showing testing methodology and key metrics (accuracy, FPS, hardware used)
• Setup guide so I can reproduce the results on my machine

Optional extras such as age estimation or full identity verification can be discussed once the core system is stable, but they are not required for this milestone.

Skills: Java, Python, Software Architecture, Machine Learning (ML), C++ Programming, Face Recognition, Image Processing, Computer Vision, Deep Learning, Model Evaluation