Computer Vision System Development

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!