Hire the Best GLSL Specialists

👋 Hi, I’m Mohamed — a Top Rated🏆 Senior Software Engineer. 🚀 I help companies design, build, and deliver high-performance software systems — from computer vision and AI pipelines to real-time desktop applications and embedded platforms. 🧩 What I deliver: ✅ Industrial Computer Vision Systems 🔹Integration with industrial cameras (Basler, Luxonis, FLIR / Point Grey) 🔹High-accuracy inspection, measurement, and defect detection 🔹Optimized OpenCV pipelines in C++ and Python 🔹Hybrid pipelines combining deep learning with classical computer vision for robustness ✅ Deep Learning (ResNet, U-Net, YOLO, MediaPipe, ...) 🔹Custom dataset creation, labeling, and curation 🔹Inference optimization for CPU, GPU, and edge devices 🔹YOLO for real-time object detection, segmentation, OBB, and pose estimation 🔹YOLO fine-tuning and transfer learning for domain-specific data 🔹MediaPipe for pose estimation, hand tracking, face landmarks, and motion analysis ✅ Real-Time & Performance-Critical Software 🔹Low-latency C++ systems optimized for speed and memory 🔹Multithreading, SIMD, profiling, and algorithmic optimization 🔹GPU acceleration (CUDA) when justified 🔹Designed for long-running, production-grade operation under real-world constraints ✅ Embedded & Edge AI 🔹Vision and AI deployment on Raspberry Pi 🔹Sensor integration, control logic, and hardware-in-the-loop testing with ESP32, Arduino ✅ Full Project Ownership 🔹System architecture and technical leadership 🔹Desktop GUIs: Qt, MFC, OpenGL 🔹Clean handover, documentation, and maintainable codebases 👉 Let's discuss how I can bring your AI, computer vision, or embedded system project to life.

Completed 50+ world-class 3D product configurators across various industries. With 20 years of experience in computer graphics and computational geometry, I help businesses turn complex 3D and visual data into interactive, production-ready software — from AI-powered computer vision pipelines to real-time 3D configurators and digital twins. On the 3D configurator and e-commerce side, I create web-based experiences where users can load, customize, and augment products in real-time using WebGL/Three.js and modern JavaScript, then purchase directly through the website — turning static product pages into interactive sales tools. My technical foundation covers both precision CAD work (STEP/IGES) and mesh-based pipelines (STL/OBJ), using geometry kernels like Parasolid, Open Cascade, CGAL, and IGL, graphics libraries like VTK and OpenGL, and languages including C++, C++/CLI, C#, and JavaScript, with UI frameworks like Qt and MFC for desktop tools. On the computer vision and AI side, I build systems that extract measurements, detect defects, and verify manufacturability directly from images, scans, or CAD models — including photogrammetry pipelines that reconstruct accurate 3D models from real-world photos for digital twin and inspection use cases. Whether the challenge is reconstructing real-world geometry through photogrammetry, building a digital twin that mirrors physical assets, training a vision model to inspect or measure parts, or shipping a configurator that helps customers buy with confidence, I bring deep geometry expertise together with modern AI and graphics tooling to deliver solutions that solve real engineering and business problems.

I build high-performance 3D web experiences that turn visitors into customers. WHAT I DO: - Product Configurators (React Three Fiber) - Real-time Physics Simulations (60+ FPS) - Interactive WebGL experiences for e-commerce RECENT PROJECTS: ✓ Liquid Chrome - Real-time physics shader engine ✓ 3D Mug Configurator - POD business tool ✓ Interactive Phoenix - WebGL character animation TECH: Three.js, React, Next.js, TypeScript, GLSL Ready to bring your 3D vision to life? Let's talk!

I design, optimize, and integrate real-time object detection and tracking pipelines for NVIDIA Jetson, RK3588, and cloud environments — with measurable gains in FPS, latency, and deployment stability. From YOLO training to TensorRT optimization and production integration, I build Computer Vision systems that work reliably on real hardware. If you need more than just a trained model — if you need a working AI system integrated into hardware or software — I deliver complete, production-ready solutions. 🎯 WHAT I DELIVER • End-to-End Deep Learning Pipelines Model architecture → dataset optimization → training → evaluation → deployment • Real-Time Object Detection & Multi-Object Tracking Optimized YOLO pipelines with stable tracking (DeepSORT / ByteTrack / BOT-SORT) • ⚡ Edge AI Acceleration & Performance Optimization TensorRT conversion, CUDA acceleration, latency reduction, memory tuning, FPS improvements • 🔗 AI Integration into Production Systems Jetson deployment, inference APIs, embedded integration, debugging, monitoring & system optimization 🏆 PROVEN EXPERIENCE • 5★ Upwork reviews for NVIDIA Jetson Nano, AGX & Orin deployments • Deployed Frigate and real-time CV pipelines on Jetson Orin NX • Optimized inference performance using TensorRT for improved real-time execution • Installed and configured LLM environments with secure key management & usage tracking • Research & production AI deployment experience at HBrain 🛠 TECH STACK Deep Learning: PyTorch, CNN architectures, YOLO variants Computer Vision: OpenCV, real-time video processing, detection & tracking Optimization: TensorRT, CUDA Systems: Linux, Embedded Systems, NVIDIA Jetson Languages: Python, C++ If you share your hardware target, dataset sample, or performance goal, I can propose a clear technical architecture and realistic delivery plan.

Real-time Computer Vision & Edge AI engineer with a deep C/C++ systems and embedded background. I build AI that runs live on real hardware - not just in notebooks. My focus is real-time video analytics on the edge: object detection, tracking, recognition, and TensorRT/CUDA optimization deployed on devices like NVIDIA Jetson. A strong C/C++, embedded, and Linux systems foundation means I care about the whole system - latency, memory movement, post-processing, UI smoothness, deployment, and failure cases - not just model accuracy. I also build LLM-powered applications that connect models to tools, documents, APIs, and business workflows (RAG, tool calling, automation). Recent work - VisionGuard AI: real-time retail shelf recognition on Jetson Orin NX - Live RTSP video analytics on edge hardware - Dense product detection with GPU-side post-processing - Stable object tracking across frames - Embedding-based SKU recognition - Unknown-product rejection instead of forced labels - Runtime recognition updates without restarting the camera stream - Smooth UI overlay under dense shelf conditions I can help with: - Real-time computer vision systems - YOLO / custom detection pipelines - TensorRT, CUDA, Jetson optimization - Multi-camera RTSP video analytics - Edge AI deployment and debugging - LLM apps with RAG, tool calling, and workflow automation - Python/C++ AI backend integration Core stack: C++ - Python - Qt6 - TensorRT - CUDA - YOLO - OpenCV - RTSP - NVIDIA Jetson - Linux

Please feel free to view some webGL proof-of-concept demos at the link below. I've worked for the past 11 years almost exclusively with webGL. Jobs have included 3d model viewers, product configurators, bone-based animation, particle effects, procedural mesh generation, multi-layer 2D sprite environments, and motion capture/motion tracking for armature animation. Prior to this I have about 2 years experience working on staff for a virtual reality lab. I can work in a number of languages - and am currently most familiar with Javascript/Typescript. I have also worked in C, C++, GLSL, Java, Python, PHP and Javascript. I tend to pick up on what I don't know quickly, and am happy to do research for project solutions. To expound upon the v.r. experience: While working on staff at the lab I was tasked with developing software for an accelerometer-based glove apparatus, learned about VR theory and methods of application, worked with a unity-like engine, and modeled in 3DStudioMax and Maya. I was also involved in the various upkeep and troubleshooting needs of a VR-capable network of nodes and projectors.