Yevhenii A.

I help engineering teams and tech founders build high-performance Computer Vision solutions, real-time perception systems, and edge AI applications. 🚀 COMPUTER VISION ENGINEER HELPING TEAMS BUILD REAL-TIME PERCEPTION SYSTEMS Implementing complex Computer Vision (CV) and deploying neural networks in real-time is a massive technical challenge. Most tech startups face these hurdles: - Low frame rates (FPS) and high latency in real-time detection. - High memory consumption that prevents deployment on Edge devices. - Difficulty porting Python-based models (PyTorch/TensorFlow) into high-performance C++ or Rust production environments. - Challenges implementing AI analysis in the browser using WebGPU or WebAssembly (Wasm). I bridge the gap between Data Science (Research) and Systems Engineering (Production), delivering optimized solutions that are fast, scalable, and robust. 🏆 RESULTS I HELP CLIENTS ACHIEVE: - Launch complex CV solutions (Object Detection, Tracking, SLAM) on time. - Increase inference performance via TensorRT, OpenVINO, or ONNX optimization. - Reduce memory footprint for deployment on NVIDIA Jetson, Raspberry Pi, and Mobile. - Build cross-platform applications for Cloud, Desktop, and Web Browsers. - Seamlessly integrate AI models into existing production infrastructure. 💻 WHAT I BUILD: - REAL-TIME VIDEO ANALYTICS: Object detection, face recognition, and behavior analysis. - EDGE AI & EMBEDDED VISION: Optimizing neural networks for resource-constrained hardware. - WEB-BASED COMPUTER VISION: High-speed model inference in the browser via Wasm/WebGPU. - 3D VISION & SLAM: Object reconstruction, robotics perception, and Point Cloud processing. - HIGH-SPEED OCR: Fast document processing and data extraction. ⚙️ TECHNOLOGIES I WORK WITH: COMPUTER VISION & ML: - OpenCV, MediaPipe - PyTorch, TensorFlow, Keras - YOLO (v8 / v10 / v11), Segment Anything (SAM), Detectron2 - Stable Diffusion PERFORMANCE & DEPLOYMENT: - C++ / Rust (High-performance processing engines) - TensorRT, ONNX Runtime, OpenVINO (Inference optimization) - CUDA / cuDNN (GPU acceleration) - WebAssembly (Wasm) & WebGPU (In-browser CV) CORE SYSTEMS & TOOLS: - Python (Research & Scripting) - Docker, NVIDIA Docker, GStreamer (Video pipelines) - Linux & Embedded Linux (Jetson, ARM) 🎯 TYPICAL SCENARIOS I HANDLE: ► Research Model to Production MVP If your model is working in a Jupyter Notebook but is too slow for real users, I rewrite critical components in C++/Rust and apply quantization/pruning to achieve production speed. ► Edge AI & Browser Migration I port models to run via WebGPU or Wasm to keep processing on the client-side, reducing cloud costs and ensuring data privacy. ► Performance Rescue If your system is dropping frames or overheating servers, I perform technical audits, implement hardware acceleration (NVDEC/VA-API), and optimize pre-processing algorithms. 🔄 MY PROCESS: 1. Architecture Design: Evaluating Accuracy vs. Speed to choose the right stack. 2. Pipeline Development: Setting up robust video ingestion and model inference. 3. Optimization: Leveraging CUDA, TensorRT, or Rust for maximum FPS. 4. Delivery: Stress testing and delivering clean, maintainable code. Ready to give your product "sight"? Drop me a message to discuss your Computer Vision project!