Hire the Best Computer Vision Engineers

Most Machine Vision projects fail between the prototype and production. I've shipped 54+ that didn't. ⚙️YOLO Detection | Pose Estimation | Object Tracking | AI Agents | LLM Integration Sports & Fitness AI | CCTV & Surveillance AI | Retail AI | Healthcare AI You have a working concept... or a clear problem involving cameras, video, or image data. The challenge is making it fast, accurate, and stable under real-world conditions. Wrong framework choices. Inference too slow for live video. Models that break the moment lighting, angle, or environment changes. And systems that detect things but can't reason about them or act on them autonomously. That's exactly where most builds stall. I design and build real-time computer vision pipelines that go all the way... from model training to live deployment... and increasingly, from visual perception to autonomous AI agents that understand, decide, and narrate. LLM APIs (OpenAI, GPT-4o, Gemini, Claude) | AWS (EC2, S3, Lambda) | Azure Cloud Services | MLOps & API Integration | Model Deployment & Scaling While most CV engineers stop at training the model, I go further: → High-speed inference optimization using TensorRT, ONNX, OpenVINO, FP16/INT8 (up to 5× faster) → LLM agents integrated with vision pipelines for alerts, reasoning, and automation → Mobile AI deployment using Core ML (iOS) and TFLite (Android) with 10+ shipped apps → Edge AI deployment on Jetson, OpenVINO, CUDA, and embedded systems → End-to-end pipelines: data → training → optimization → real-time deployment Key Accomplishments: ⭐ $5M+ revenue from AI solutions ⭐ 100+ computer vision systems delivered ⭐ Built and launched 2 SaaS products ⭐ Real-time sports AI (7+ sports, 15+ teams) ⭐ 10+ mobile AI apps (iOS Core ML, Android TFLite) ⭐ Production AI for surveillance, industrial & safety use cases ⭐ Medical imaging AI deployed in 5+ hospitals ⭐ Up to 5× faster inference (ONNX, TensorRT, FP16/INT8) ⭐ Large-scale tracking & re-ID (1M+ labeled data) ⭐ Agentic AI systems for autonomous decision-making If you have read this far, please note that I appreciate you taking the time to learn about me. Personally, it’s been an amazing journey and knowledge exercise to get to this level of competence in AI and software development. Domain Expertise: ✅ athlete tracking | shot detection | scoring | drill analysis | pose estimation ✅ defect inspection | PPE compliance | staff monitoring | meter reading | quality control ✅ ANPR | crowd monitoring | people counting | intrusion detection | perimeter security ✅ tumor detection | ultrasound | X-ray/CT analysis | lesion segmentation | medical imaging ✅ aerial monitoring | traffic flow | license plate recognition | vehicle & accident detection ✅ customer analytics | receipt extraction | shelf monitoring | inventory tracking Tech Stack: YOLOv5–YOLOv8–YOLOv11, Detectron2, MMDetection, DeepSORT, StrongSORT, MediaPipe, OpenPose, Pose Estimation, Action Recognition, Segmentation (semantic & instance), OCR, anomaly detection, object tracking, PyTorch, TensorFlow, TFLite, Core ML, OpenCV, FastAPI, Flask, ONNX, TensorRT, OpenVINO, CUDA, AWS, Azure, GCP, edge AI, mobile AI, real-time inference, video analytics, AI automation, LLM integration (GPT-4o, Claude, Gemini, Groq), LangChain, LangGraph, CrewAI, RAG systems. 💬 If your project involves cameras, video, or images... and you need it fast, accurate, fully deployed, and intelligent enough to reason and act autonomously... I am the engineer you are looking for.

Most computer vision projects fail not in training — but in deployment. Models that hit 95% accuracy in the lab break down when lighting shifts, hardware stutters, or the camera feed isn't clean. I build systems engineered to survive those conditions — and I've done it across industries, hardware platforms, and deployment environments. I'm a Computer Vision Engineer specializing in end-to-end AI pipelines — from raw camera input to real-time inference, deployed on edge hardware, cloud APIs, or both. ━━ Core services ━━ → Object detection & multi-object tracking — YOLOv8, YOLOv5, ByteTrack, BOTSort, MMDetection → Segmentation, pose estimation & keypoints — MediaPipe, custom model architectures → Edge AI deployment — NVIDIA Jetson Orin/Nano, Raspberry Pi, Hailo — TensorRT, ONNX, INT8/FP16 → Cloud & API deployment — FastAPI, Docker, AWS GPU instances, REST & WebSocket inference APIs → Video analytics & smart camera systems — safety monitoring, defect detection, zone tracking, people counting ━━ Systems I've shipped ━━ ✓ Real-time fall detection on NVIDIA Jetson — production-deployed, sub-100ms latency ✓ Zone-based people tracking & monitoring for safety-critical environments ✓ Industrial defect detection pipeline — TensorRT-optimized, running on constrained edge hardware ✓ End-to-end smart camera system: camera → inference → dashboard & real-time alerts ✓ OpenCV video analytics pipelines with custom pre/post-processing and business logic ━━ What makes my work different ━━ Most CV engineers deliver a model file. I deliver a working system — optimized, integrated, and running reliably in your environment. I lead a small team and personally own system architecture, optimization strategy, and core AI engineering on every project. You get senior-level technical execution, not delegation to juniors. Edge or cloud. Jetson or GPU server. Prototype or production scale. I've built across all of it. ━━ How a typical project runs ━━ 1. Discovery — review your hardware targets, data sources, and latency requirements before any code is written 2. Architecture — design the full pipeline: model selection, optimization path, deployment stack, integration points 3. Build & optimize — iterative development with benchmarked FPS and accuracy metrics at each stage 4. Deployment — containerized, documented, and running on your target environment 5. Handover — clean codebase, inline documentation, and a session so your team can maintain it independently ━━ Full tech stack ━━ Models: YOLOv8, YOLOv5, YOLOv7, MMDetection, Detectron2, PyTorch, TensorFlow, ONNX Runtime Tracking: ByteTrack, BOTSort, DeepSORT, StrongSORT, custom zone logic & counting algorithms Optimization: TensorRT INT8/FP16, ONNX quantization, model pruning, batch inference tuning Edge hardware: NVIDIA Jetson Orin/Nano, Raspberry Pi 4/5, Hailo-8, Coral TPU Cloud & infra: FastAPI, Flask, Docker, AWS EC2/Lambda, GCP, RTSP/RTMP stream processing Vision utilities: OpenCV, FFmpeg, GStreamer, PIL/Pillow, custom pipeline components ━━ Project types I take on ━━ → Greenfield CV systems — full pipeline from scratch to production deployment → Model optimization — take an existing model and make it production-fast on your hardware → Edge porting — migrate a cloud-based CV system to Jetson, Raspberry Pi, or Hailo → Pipeline debugging — diagnose and fix latency, accuracy, or stability issues in live systems → Inference API — wrap your CV model as a scalable, low-latency REST or WebSocket API → PoC → production — take a working demo and harden it for real-world deployment at scale → Team augmentation — embedded senior CV engineer for sprints or longer-term engagements ━━ Industries served ━━ Manufacturing & quality control — defect detection, visual inspection, production line monitoring Safety & security — real-time threat detection, perimeter monitoring, crowd analytics Retail & logistics — shelf analytics, people counting, queue management, warehouse tracking Healthcare — patient monitoring support systems, lab automation, medical imaging pipelines Agriculture — crop health detection, drone-based aerial inspection, field monitoring systems ━━ Common questions ━━ Work with our existing dataset? Yes — I assess quality, recommend augmentation strategies, and fine-tune models on your labeled data. Edge or cloud deployment? Both — Jetson, Raspberry Pi, and Hailo at the edge; AWS GPU instances and containerized APIs in the cloud. Can you take our prototype to production? That's one of my most common engagements — hardening, optimizing, and deploying existing concepts for real-world reliability. Documentation and handover included? Always. Clean code, inline comments, deployment instructions, and a dedicated handover session on every project. If you need computer vision that performs beyond lab conditions — on real hardware, with real data, in real-world environments — let's talk.

I'm a Senior Computer Vision Engineer with 7+ years of professional experience delivering enterprise-grade Computer Vision, Edge AI, Deep Learning & Machine Learning Solutions. I have a solid foundation in state-of-the-art deep learning models and machine learning algorithms, applying them to build, deploy, and optimize production-ready systems across edge devices, cloud platforms, and agentic AI pipelines. ✅ What I Build ▸ Computer Vision: object detection, tracking, segmentation, pose estimation, image classification, counting, OCR, anomaly detection ▸ Edge AI: NVIDIA Jetson (Nano, Orin, Xavier), Raspberry Pi, model optimization with TensorRT, ONNX, and TFLite for real-time inference ▸ Image Generation: Stable Diffusion (SDXL, SD 3.5), Flux, DALL·E 3, ControlNet, LoRA fine-tuning, ComfyUI workflows ▸ Video Generation: Kling, Runway Gen-3, Minimax Hailuo, Veo, Pika, automated cinematic and product video pipelines ▸ AI Agents: autonomous multi-step agents using LangChain, LangGraph, CrewAI, and AutoGen with RAG, memory, and tool use ▸ Voice AI: conversational voice agents using Vapi, Retell AI, ElevenLabs, Bland AI, and Deepgram for inbound/outbound calling and automation ▸ MCP Servers: custom Model Context Protocol servers connecting Claude and other agents to your APIs, databases, and internal tools ▸ Claude Code: agentic software engineering with subagent architectures, skill-based pipelines, and autonomous multi-step coding workflows ✅ Tech Stack ▪ Computer Vision: OpenCV, YOLO, MediaPipe, Detectron2, SAM, Vision Transformers ▪ Deep Learning: PyTorch, TensorFlow, Keras, ONNX ▪ Tracking & Optimization: DeepSORT, ByteTrack, TensorRT, OpenVINO ▪ Deployment: DeepStream, Triton Inference Server, TFLite, FastAPI, Flask, Docker ▪ Generative AI: Stable Diffusion, Flux, ComfyUI, Replicate, Fal.ai ▪ Agents & Voice: LangChain, LangGraph, CrewAI, Vapi, Retell AI, ElevenLabs, n8n ▪ MCP & Agentic: Claude Code, MCP SDK, custom tool servers ▪ Cloud: AWS, GCP, Azure ▪ Languages: Python, C++, CUDA 🚀 Send me a message with what you are trying to build, and let's discuss.

⭐️Top Rated Plus — Top 1% on Upwork ⭐️ Over 100 Enterprise LLM and Computer Vision Solutions Delivered 💸 $5 Million+ Generated in revenue for top companies worldwide 🥇Gold Medalist in Computer Engineering & Microsoft Imagine Cup Winner I’m a Senior Computer Vision and AI, and Full-Stack Developer with 8+ years of experience building production-grade AI systems, Large Language Model (LLM) solutions, intelligent chatbots, and computer vision applications for startups and enterprises worldwide. I’ve helped 90+ companies across the US, Europe, and the Middle East launch scalable AI products and generate over $5M in revenue through automation, predictive systems, and generative AI. Currently, I lead Aeyron Technologies Pvt. Ltd. as CEO while delivering high-impact freelance AI solutions for global clients. If you’re looking for someone who can design, build, fine-tune, and deploy real AI systems and not demos, you’re in the right place. LLMs, Generative AI & Chatbots: • LLM fine-tuning and custom model training (LLaMA, LLaMA-2, BLOOM, OPT) • Prompt engineering and workflow optimization • RAG systems using LangChain, FAISS, ChromaDB • AI chatbot development and automation agents • API-based AI integrations for web and mobile applications Common use cases: AI assistants, document intelligence, knowledge-base bots, SaaS AI features, automated workflows. Computer Vision Expertise: • Object detection and tracking (YOLO, OpenCV, custom deep learning models) • OCR systems and document processing • Face recognition and biometric systems • Image segmentation and analytics • Video intelligence pipelines • 3D vision and stereo reconstruction • AR/VR vision-based applications Machine Learning & AI Engineering: • Predictive modeling and forecasting • NLP systems and text analytics • Time-series analysis • Reinforcement learning • Data pipelines and MLOps • AI automation tools • AI-powered dashboards and products Full-Stack & AI Product Development: Frontend: React, Angular, Flutter, Streamlit, Tailwind, Figma Backend: Node.js, Django, Flask, .NET, REST APIs Databases: MongoDB, PostgreSQL, MySQL, Firebase, SQL Server Cloud & DevOps: AWS, GCP, Azure, Docker, Kubernetes, Nginx, Heroku I deliver end-to-end AI products from MVP to enterprise scale. AI/ML Tech Stack: PyTorch, TensorFlow, Keras, OpenCV LangChain, LlamaIndex, FAISS, ChromaDB NumPy, Pandas, Scikit-learn, Matplotlib AWS SageMaker, Rekognition, GCP Vision API Why Clients Choose Me: - Top Rated Plus freelancer (Top 1%) - Proven $5M+ revenue impact - Production-grade AI systems - Clear communication and fast delivery - Business-focused AI solutions Typical Projects: • Custom LLM fine-tuning and private GPT systems • AI chatbots for SaaS and customer support • Computer vision pipelines • AI-powered SaaS platforms • Intelligent data products • AI workflow automation

👋 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.

With 5+ years of experience and 150+ successful projects, I help businesses build high-performance Computer Vision and AI Agent systems that work in production — not just in theory. 🚀 What I Build ✔ AI Agents & Automation Pipelines (OpenClaw, LangChain, CrewAI, AutoGen) ✔ Semantic Search & RAG Systems using vector databases (FAISS, pgvector, OpenSearch) ✔ Personal AI Assistants with persistent memory & full system access ✔ Object Detection & Multi-Object Tracking (YOLO26, YOLOv12, YOLO11, YOLOv8, DeepSORT, ByteTrack, BOT-SORT) ✔ Real-Time Video Analytics & Surveillance Systems ✔ Face Recognition & Liveness Detection ✔ Image Segmentation (U-Net, DeepLabV3+, Semantic & Instance) ✔ OCR & Document AI (Tesseract, Google Document AI, PaddleOCR) ✔ Industrial Defect Detection & Quality Control ✔ Medical Image Analysis ✔ Traffic & Vehicle Detection Systems ✔ Retail Analytics & Customer Behavior Tracking ✔ Edge AI Deployment (Jetson, TensorRT, CUDA, Docker, AWS) ✔ Model Optimization (FPS, latency, memory efficiency) ⚡ What I Deliver ✔ End-to-end AI systems (data pipelines → model serving → deployment → monitoring) ✔ LLM and AI agent architectures (RAG, tool use, function calling, multi-agent workflows) ✔ Semantic search and vector database solutions (OpenSearch, FAISS, pgvector) ✔ Real-time computer vision systems (detection, classification, tracking, segmentation) ✔ Custom YOLO model training on your own dataset (YOLOv8, YOLO11, YOLO26) ✔ Multi-camera surveillance & smart monitoring systems ✔ Video analytics pipelines with real-time alerting & reporting ✔ Scalable AI infrastructure on AWS (SageMaker, EKS, Lambda, EC2) ✔ Production-grade APIs and backend services ✔ Optimization of existing AI systems (lower latency, reduced cloud costs, improved reliability) 🧠 Core Expertise Computer Vision · AI Agents · OpenClaw · Deep Learning · Machine Learning · Object Detection · Multi-Object Tracking · Image Segmentation · Real-Time AI · Video Analytics · OCR · Data Annotation · Edge AI · Generative AI · LLM Integration · RAG Systems 🛠 Tech Stack AI & Vision: PyTorch · TensorFlow · Keras · OpenCV · MediaPipe · YOLO variants · Faster R-CNN · Vision Transformers AI Agents: OpenClaw · LangChain · CrewAI · AutoGen · RAG · LLMs · GPT-4 · Gemini Tracking & Optimization: DeepSORT · ByteTrack · BOT-SORT · TensorRT · CUDA Backend & Deployment: FastAPI · Flask · Docker · AWS · Jetson · REST APIs 🌍 Industries I Serve Retail · Security & Surveillance · Healthcare & Medical · Industrial & Manufacturing · Traffic Management · Smart Cities · Agriculture · Sports Analytics 💡 Why 150+ Clients Chose Me ✔ 100% Job Success Score — Top Rated on Upwork ✔ 5+ years delivering real-world AI systems ✔ Production-ready, scalable solutions ✔ Strong optimization — high FPS, low latency ✔ Clear communication & on-time delivery 📩 Let's Work Together Looking to build a Computer Vision system, AI Agent, Object Detection model, or Real-Time AI solution? 👉 Message me now — I'll help you design the best approach and deliver a scalable, production-ready solution fast.