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.

- 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!

Design and deploy AI pipelines that transform image, video, and audio data into reliable real-world applications, from computer vision analytics and 3D perception to audio (speech, music, sound) analysis and generative AI systems. My work focuses on production AI systems, not research prototypes. I help companies move from early feasibility studies and PoC development to scalable deployments running in real environments. Typical projects include: • computer vision pipelines for detection, tracking, and segmentation • real-time video analytics and edge AI deployments • 3D vision and spatial perception systems • speech processing and audio AI solutions • generative AI pipelines for image, video, and audio Computer Vision Systems Design and development of advanced computer vision pipelines for image and video analysis. Typical solutions include: • object detection and multi-object tracking • semantic and instance segmentation • pose estimation and motion analysis • OCR and document understanding • visual search and recognition systems These systems are used in industries such as manufacturing, sports analytics, retail, healthcare, and security. 3D Vision & Spatial AI Development of AI systems that understand spatial structure and depth. Experience includes: • structure-from-motion (SfM) • photogrammetry pipelines • depth estimation models • NeRF and neural rendering • point cloud processing and 3D reconstruction Applications include robotics, AR/VR, construction analytics, and digital twins. Edge AI & On-Device ML I specialize in deploying ML models on mobile and embedded devices where latency, memory, and power constraints are critical. Typical optimization techniques include: • model quantization and pruning • architecture optimization • real-time inference pipelines • deployment on mobile and embedded hardware Technologies include: TensorRT, TensorFlow Lite, CoreML, ONNX Runtime. Many deployed systems operate with 50–100 ms inference latency depending on hardware. Generative AI for Vision & Video Development of generative pipelines for media processing and synthetic data generation. Typical solutions include: • image and video generation pipelines • diffusion-based editing and enhancement • synthetic dataset generation for model training These tools help accelerate AI training and improve model robustness. Audio & Speech AI Development of AI systems for speech processing, audio analysis, and voice technologies. Examples include: • phoneme segmentation and pronunciation analysis • speech recognition pipelines • voice feature extraction and audio analytics • generative audio and music models These systems are used in: • language learning platforms • speech therapy tools • voice biometrics systems • music AI applications Technical Stack Frameworks & Models PyTorch, TensorFlow, OpenCV, Detectron2, MediaPipe, YOLO, DINO, SAM, CLIP Deployment TensorRT, TensorFlow Lite, CoreML, Docker, ONNX Runtime, FastAPI Programming Python, C, C++ 3D Vision NeRF, SLAM, Dust3r, point clouds Leadership & R&D I lead an R&D-focused AI team at It-Jim, an AI consulting company with 30+ engineers and 10+ PhDs specializing in: • Computer Vision • Generative AI • Audio & Speech AI • Edge AI systems We help companies solve technically challenging AI problems and build reliable production systems. If you are looking for experienced AI engineers to design, prototype, or deploy advanced machine learning solutions: feel free to reach out!

⭐️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

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.