Computer Vision System Development
Budget: €750 – €1,500 EUR
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!
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!