Hire the Best Computer Vision Engineers

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.

- CTO at Singularis Programming - Extensive experience in Computer Vision and Machine Learning projects. - Keen on cutting-edge technologies and helping businesses achieve their objectives. 🏅About company - 13+ years of continuous experience in programming - Our engineers got significant achievements and results in the Programming World Cup ACM ICPC - 200+ successfully completed projects 🤝 Trusted by Intel, NEC, Samsung, DHL, VIATechnik, Amberg Technologies - Working with partners around the world, in all time zones: in Europe, North America, MENA, the Asia-Pacific region. - Completed projects in the UK, Switzerland, the USA, Spain, Germany, Belgium and other of EU. 💡 OUR SERVICES include, but are not limited to: – Definition and Planning, Business Analysis, Project Planning – Back-end development + DevOps Application Architecture, REST API, Application development, CI/CD, .NET Stack, C#, Entity Framework, NHibernate, Microservices – Front-end development Mark-upping, Client-side development, Responsiveness, React, Typescript, JavaScript, Redux, MobX – AI / Machine Learning / LLM Deep Learning and convolutional networks, GAN, TensorFlow, Keras, Torch, Stable Diffusion, Transformer architectures, fine-tuning and optimization, Hugging Face, LangChain, RAG, prompt engineering, Unsloth, llama.cpp, vLLM, model quantization and deployment – Computer Vision OpenCV, C++/Python, MMCV, YOLO, dlib, developing applications for Kinect and Intel RealSense, Microsoft HoloLens, video processing with FFmpeg – Mobile development iOS, Android, Cross-platform – Testing/Quality Assurance Manual (TestLink), Unit Tests, Automation, Stress, checklists, test-cases, Cypress – Technical Support and Maintenance DB Back-ups, Cloud/Data migration, Bug Fixing, 24/7 support 💼 Main principles - Maximal quality of the provided services and the developed software. - Focus on permanent improvement of our team. - Stay tuned at the front edge of modern computer technologies - Apply the most advanced approaches to software development. 👋🏼 I’m open to collaborate. Feel free to get in touch with me and my team to discuss any project implementation — we’ll be happy to share valuable ideas and suggestions!

Most machine learning projects fail between the prototype and production. I've shipped 47+ that didn't. 🎯 YOLO Detection | 🧍 Pose Estimation | 🏋️ Sports AI | 🛒 Retail AI | 🛡️ CCTV Analytics | 🔄 Tracking | 🧠 ML Pipelines | 🤖 AI Agents | 💬 LLM Integration 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. Object detection · Machine learning · Pose estimation · Multi-camera tracking · Segmentation · Re-identification · Anomaly detection · OCR & ANPR · Optical flow · Depth estimation · LLM-powered reasoning · Agentic decision pipelines While most CV engineers stop at training the model, I go further: → Accelerated inference with TensorRT, ONNX, OpenVINO, and FP16/INT8 quantization (up to 5× faster) → LLM agents layered over CV pipelines for real-time decisions, alerts, and natural language outputs → Mobile deployment via CoreML (iOS) and TFLite (Android) with 10+ live apps shipped → Edge deployment on Jetson, OpenVINO, Apple Neural Engine, and CUDA/cuDNN → End-to-end pipeline: camera input → training → optimization → real-time actionable output Key Accomplishments: ⭐ Generated $5M+ in client revenue ⭐ Delivered 100+ end-to-end computer vision systems ⭐ Successfully launched my own 2 SaaS products ⭐ Real-time sports AI for 7+ sports, improving analytics for 15+ teams ⭐ Mobile AI on iOS (Core ML) & Android (TFLite), powering 10+ apps ⭐ Surveillance, safety, and industrial AI solutions ⭐ Medical imaging AI for 5+ hospitals: tumor detection, ultrasound, test strips ⭐ Model optimization: up to 5× faster inference using FP16/INT8, ONNX, TensorRT, OpenVINO ⭐ Multi-object tracking, re-identification, Model Training 1M+ labelled Dataset ⭐ Agentic CV systems that perceive, reason, and act without human input in the loop 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: - Sports & Fitness: athlete tracking, shot detection, scoring automation, drill analysis, pose estimation - Industrial & Workplace: tire defect inspection, PPE compliance, staff monitoring, meter reading, machine vision inspection, automated quality control - Surveillance & Security: ANPR, crowd monitoring, people counting, animal attack detection, exam cheating detection, perimeter security, intrusion detection - Healthcare & Medical: tumor detection, ultrasound processing, test strip analysis, X-ray/CT scan processing, lesion segmentation, medical image annotation - Traffic & Transport: aerial monitoring, traffic flow AI, license plate recognition, vehicle detection, accident detection, parking management - Retail & Business: customer analytics, receipt extraction, retail intelligence, object recognition, shelf monitoring, inventory management Tech Stack: Machine Learning, Deep Learning, YOLOv5, YOLOv8 - YOLO26, Detectron2, DeepSORT, StrongSORT, MMDetection, MediaPipe, OpenPose, PoseTrack, Action Recognition, Semantic Segmentation, Instance Segmentation, OCR, Anomaly Detection, Motion Detection, Object Counting, License Plate Recognition, PyTorch, TensorFlow, TensorFlow Lite, Keras, OpenCV, FastAPI, Flask, Core ML, TFLite, ONNX, TensorRT, OpenVINO, CUDA, Swift, Kotlin, Flutter, Python, C++, AWS, GCP, Azure, Edge Deployment, Mobile AI, Real-Time Inference, Surveillance AI, Aerial Drone Analytics, Video Stream Analytics, AI Automation, LLM Integration (GPT-4o, Claude, Gemini, Groq), AI Agent Frameworks (LangChain, LangGraph, CrewAI), RAG Pipelines, Streaming LLM Inference license plate recognition, aerial drone analytics, surveillance AI, mobile AI, embedded systems, deep learning pipelines, inference optimization, video stream analytics, AI automation, AI for industry 4.0, computer vision pipelines. 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.

Expert-Vetted (top 1%) AI consultant. 100% JSS across 13 contracts. Computer Vision and LLM systems shipped to production — not prototypes. What I build: → Edge CV at scale: 30 FPS sustained on Jetson Orin Nano, INT8 TensorRT, IP67 enclosures. Production deployments include fish counting and defect detection (99.4% accuracy on holdout) and aerial imagery analysis with VLM + YOLO11 hybrid pipelines. → Agentic AI for regulated industries: cybersecurity vendor risk scoring (SOC2/NIST/ISO mapping), AI legal study tools (20K+ case briefs), and AI financial intelligence platforms. Stack: LangGraph, Claude Opus 4.7, Bedrock AgentCore, MCP servers, pgvector. → Voice AI that respects users: trauma-informed prompt design for wellness and healthcare. Vapi/Twilio + Claude/GPT-4. Latency under 800ms. GDPR/HIPAA-aligned. → Fractional CTO for AI startups raising seed-to-Series-A: architecture review, hiring plans, AWS infrastructure, technical due diligence. How I work: I run a small Canadian team (4 senior engineers) and use AI-augmented development to ship 2-3x faster than typical agencies at $100-200/hr blended rates. Most engagements start with a 1-2 week paid discovery ($2K-5K fixed) so we can scope accurately before committing to MVP. What I don't do: WordPress, generic web apps, ChatGPT wrappers without retrieval, or projects under $5K (those are better suited to mid-tier freelancers). Available 30+ hrs/week. Burnaby BC, work primarily on PT/ET timezones. 20-minute discovery call: send the job link.

👋 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.