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I build real-time AI systems that detect threats, track objects, and turn video into actionable insights ready for real-world deployment. If you need a reliable Computer Vision solution (not just a demo), I can design, train, and deploy it end-to-end. 🎯 What I Do I specialize in building production-ready Computer Vision systems that work in real environments not just controlled demos. From surveillance and safety to traffic analytics and automation, I help businesses turn video data into practical, usable intelligence. 🔧 Solutions I Build ✔ Real-Time Object Detection (YOLOv8 / YOLO11) ✔ Multi-Object Tracking (ByteTrack, DeepSORT) ✔ Surveillance & Threat Detection Systems ✔ Fire & Smoke Detection ✔ PPE / Safety Compliance Monitoring ✔ Traffic Monitoring & Speed Estimation ✔ OCR & Document AI ✔ End-to-End AI Systems (Data → Training → Deployment) 🧠 Why Clients Choose Me Most freelancers stop at training a model. I go further — I build complete systems that actually work in production. ✔ Optimized for real-time performance (high FPS, low latency) ✔ Designed for real-world conditions (low light, fog, motion blur) ✔ Strong focus on data quality & annotation (where most projects fail) ✔ Clean, scalable, deployment-ready code 💡 Most Computer Vision models fail outside the lab — I make sure yours doesn’t. 📊 Selected Projects 🔫 Real-Time Weapon Detection & Tracking AI-powered surveillance system for detecting and tracking firearms in live video streams → Helps improve security response time and monitoring 🔥 Real-Time Fire & Smoke Detection (107 FPS) Early hazard detection system designed for fast response in critical environments → Detects fire/smoke in real-time to reduce risk and damage 🦺 Construction Safety Monitoring (PPE Detection) Helmet detection system with live violation alerts → Improves worker safety and compliance on-site 🚗 Vehicle Speed Estimation System Tracking-based system for real-time speed analysis from video → Useful for traffic monitoring and smart city solutions ⚙️ Tech Stack AI / Deep Learning: PyTorch, YOLO (v8, v11), TensorFlow Computer Vision: OpenCV, real-time video processing pipelines Tracking: ByteTrack, DeepSORT Deployment: FastAPI, Flask, Docker, GPU acceleration Language: Python 🎬 What You Can Expect ✔ Demo available before starting ✔ Clean, well-documented code ✔ Fast communication & regular updates ✔ Scalable solutions ready for deployment ✔ Support with real-world challenges (lighting, motion, noise) 💡 How I Work I follow a complete pipeline: Data Collection → Annotation → Training → Optimization → Deployment You don’t just get a model you get a working system ready to use. 📩 Let’s Build Something Real Have an idea or project in mind? 👉 Send me a message I’ll break down the best approach and can even share a quick demo or plan before you commit.

Computer Vision Engineer specializing in real-time detection systems across surveillance AI, manufacturing inspection, medical imaging, and smart city infrastructure — deployed to production. I build end-to-end: camera input, model training, TensorRT optimization, edge or cloud deployment, and backend integration. Not prototypes. Working systems. WHAT I HAVE SHIPPED: Multi-tenant AI surveillance platform with 8 real-time detection agents — intrusion, loitering, occupancy, vehicle counting, LPR, fire/smoke detection, entry after hours, and late checkout. YOLO11s + PaddleOCR on RunPod GPU, RTSP stream management via MediaMTX, FastAPI backend on DigitalOcean, CI/CD via GitHub Actions with a self-hosted runner. Supabase for auth and Postgres. Full production deployment. Spine X-ray segmentation system — 25-class vertebrae labeling (C1 through L5) with automated clinical metric extraction: disc height, segment angle, neutral listhesis, interspinous gap. Dual architecture: ResNet34 U-Net baseline and a custom ConvNeXt-Tiny encoder with CBAM attention trained on real X-rays and synthetic DRR data from CT volumes. 0.92 Dice on full-spine segmentation. Real-time defect detection on NVIDIA Jetson Xavier for a food and beverage production line. TensorRT FP16 inference, zero cloud dependency, PLC integration for automated pass/fail. Defect catch rate improved significantly over manual baseline. LiDAR-camera sensor fusion pipelines for smart city traffic monitoring — ROS, DeepStream, NVIDIA Jetson, production-deployed infrastructure. CORE CAPABILITIES: → Detection, segmentation, tracking: YOLO variants, SAM2, custom PyTorch → TensorRT optimization: FP16/INT8 quantization, ONNX export, engine serialization → Edge deployment: Jetson Nano, NX, Orin, Xavier — hardware bring-up to production → Medical imaging: U-Net architectures, multi-class segmentation, DICOM, clinical metrics → Surveillance AI: multi-agent detection, RTSP streams, LPR, event-based alerting → Industrial integration: FastAPI, MQTT, EMQX, Azure IoT Hub, AWS S3, Docker, Supabase INDUSTRIES: Manufacturing quality control — food and beverage, general production lines Surveillance and security — multi-camera platforms, license plate recognition, event detection Medical imaging — vertebral segmentation, clinical measurement pipelines Smart city and traffic — LiDAR-camera fusion, intrusion detection, infrastructure monitoring I communicate clearly, surface blockers early, and deliver systems that run reliably in your environment.

Hello, I’m the founder of StreamTech, with over 11,000 hours across more than 100 AI and machine learning projects since 2016. I bring deep expertise in computer vision and edge AI ranging from object detection, tracking, and OCR to pose estimation, generative image processing, and event detection in sports feeds. I also excel in building intelligent AI agents and LLM driven chatbots that leverage multi turn dialog, RAG enabled memory systems, and API orchestration. My offerings extend beyond AI models and edge deployment. I provide full mobile experience solutions, creating cross platform React Native apps complemented by thoughtful UI/UX design. Whether it's crafting intuitive interfaces, responsive layouts, or seamless animations tailored for both iOS and Android, I ensure that the user experience complements the underlying AI technology. I guide projects end to end, collecting and labeling data, architecting and training models with PyTorch and TensorFlow, and deploying solutions either in the cloud (AWS, GCP) or on edge devices like Jetson Nano, Xavier, and Orin using DeepStream SDK. My engineering stack includes Python, C++, OpenCV, MediaPipe, OpenPose, SMPL, GANs, Stable Diffusion, Docker, and Kubernetes. At StreamTech, our mission has always been to harness cutting edge tech for meaningful business impact. By blending AI innovation with elegant mobile design, I help entrepreneurs and managers accelerate product development. If you're envisioning a mobile solution powered by CV or conversational AI, or need an AI agent interface that shines on mobile, let’s connect and explore how we can craft something exceptional together. Cheers!!

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.

𝐈 𝐛𝐮𝐢𝐥𝐝 𝐀𝐈 𝐩𝐫𝐨𝐝𝐮𝐜𝐭𝐬 𝐭𝐡𝐚𝐭 𝐦𝐨𝐯𝐞 𝐟𝐫𝐨𝐦 𝐝𝐞𝐦𝐨 𝐭𝐨 𝐝𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭. I bring 3+ years of experience in the following; 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 ✅ Real-time video analysis and camera-based AI ✅ Object detection, image segmentation, and classification ✅ Pose/keypoint analysis, OCR, visual inspection, and anomaly detection ✅ Image-processing and preprocessing pipelines [OpenCV, YOLO, MediaPipe, PyTorch, U-Net, DeepLab, SAM, PaddleOCR, EasyOCR, Tesseract] 𝐈𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧 & 𝐀𝐈 𝐃𝐞𝐩𝐥𝐨𝐲𝐦𝐞𝐧𝐭 ✅ Model serving and latency reduction ✅ Scalable APIs and Dockerized environments ✅ Local, cloud, and GPU-based deployment ✅ Performance profiling and deployment-ready architecture [NVIDIA Triton, TensorRT, ONNX Runtime, FastAPI, Docker, CUDA, REST APIs, AWS, RunPod] 𝐆𝐞𝐧𝐞𝐫𝐚𝐭𝐢𝐯𝐞 𝐀𝐈 ✅ Custom image-generation workflows ✅ LoRA training and fine-tuning ✅ Stable Diffusion integrations and image-editing pipelines ✅ API-based GenAI applications [ComfyUI, SDXL, Stable Diffusion 1.5, FLUX, LoRA, Hugging Face Diffusers, ControlNet, IP-Adapter] 𝐃𝐨𝐜𝐮𝐦𝐞𝐧𝐭 𝐀𝐈 & 𝐋𝐋𝐌 𝐖𝐨𝐫𝐤𝐟𝐥𝐨𝐰𝐬 ✅ OCR and structured document extraction ✅ RAG applications and internal AI assistants ✅ Compliance workflows and report generation ✅ LLM-powered review and automation systems [OpenAI, Claude, GPT, LangChain, Hugging Face Transformers, embeddings, vector search, structured JSON outputs] 𝐒𝐞𝐥𝐞𝐜𝐭𝐞𝐝 𝐫𝐞𝐬𝐮𝐥𝐭𝐬 📌 Built exercise and analytics modules for a real-time posture assessment product with custom keypoint-based analysis and multi-client performance around 24 FPS 📌 Reduced an industrial visual-inspection workflow from roughly 40 seconds(manual human inspection) to around 1.5 seconds 📌 Reduced image background removal pipeline inference time with onnxruntime optimizations, cutting processing time for 4k images from 7 seconds to 1.5 seconds 📌 Built OCR and LLM workflows that reduced manual document sorting and compliance review time from one hour to under 15 minutes. 📌 Delivered confidential Triton-based model-serving and scalable inference work I focus on clear communication, measurable improvements, clean code, and a handoff your team can actually use. If you need a Computer Vision system, GenAI workflow, or AI product that needs to run reliably outside a notebook, I can help you build, optimize, and deploy it. /// SEO tags /// #computer vision #computer vision engineer #computer vision #OpenCV #machine learning computer vision #deep learning computer vision #computer vision machine learning #machine learning python #nlp machine learning #ComfyUI #mediapipe #edge AI #Nvidia Triton #TensorRT #AWS

→ 𝐓𝐨𝐩 𝐑𝐚𝐭𝐞𝐝 𝐅𝐫𝐞𝐞𝐥𝐚𝐧𝐜𝐞𝐫 | 𝟏𝟎𝟎% 𝐉𝐨𝐛 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 𝐒𝐜𝐨𝐫𝐞 → 𝐏𝐡𝐃 𝐢𝐧 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 | 𝐈𝐧𝐝𝐮𝐬𝐭𝐫𝐲 & 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡 𝐄𝐱𝐩𝐞𝐫𝐭𝐢𝐬𝐞 → 𝟏𝟓+ 𝐘𝐞𝐚𝐫𝐬 𝐨𝐟 𝐏𝐫𝐨𝐯𝐞𝐧 𝐄𝐱𝐩𝐞𝐫𝐢𝐞𝐧𝐜𝐞 𝐢𝐧 𝐀𝐈, 𝐌𝐚𝐜𝐡𝐢𝐧𝐞 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 & 𝐂𝐨𝐦𝐩𝐮𝐭𝐞𝐫 𝐕𝐢𝐬𝐢𝐨𝐧 → 𝐓𝐫𝐮𝐬𝐭𝐞𝐝 𝐭𝐨 𝐃𝐞𝐥𝐢𝐯𝐞𝐫 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐢𝐨𝐧-𝐑𝐞𝐚𝐝𝐲 𝐀𝐈 𝐒𝐨𝐥𝐮𝐭𝐢𝐨𝐧𝐬 𝐟𝐨𝐫 𝐆𝐥𝐨𝐛𝐚𝐥 𝐂𝐥𝐢𝐞𝐧𝐭𝐬 → 𝐏𝐮𝐛𝐥𝐢𝐬𝐡𝐞𝐝 𝐢𝐧 𝐂𝐕𝐏𝐑, 𝐌𝐈𝐂𝐂𝐀𝐈, 𝐖𝐀𝐂𝐕, 𝐓𝐏𝐀𝐌𝐈, 𝐈𝐉𝐂𝐕 & 𝐈𝐂𝐑𝐀 I am a Senior AI/ML and US-trained Computer Vision Engineer with over 15 years of hands-on experience building, training, and deploying production-ready AI solutions. I specialize in Computer Vision, Deep Learning, Machine Learning, Medical Imaging, Healthcare AI, and Image Processing, delivering scalable and real-time AI systems using Python, modern deep learning frameworks, and optimized deployment pipelines for real-world applications. I hold a PhD in Computer Vision and have worked across industry and research on AI projects involving object detection, image segmentation, video analytics, OCR, medical imaging, surveillance systems, healthcare diagnostics, and intelligent automation. My focus is on end-to-end AI development, from data preprocessing and model training to deployment, API integration, and production-ready, scalable AI systems. I have also published numerous peer-reviewed research articles in leading computer vision and AI venues including 𝗖𝗩𝗣𝗥, 𝗠𝗜𝗔, 𝗠𝗜𝗖𝗖𝗔𝗜, 𝗪𝗔𝗖𝗩, 𝗜𝗘𝗘𝗘 𝗧𝗣𝗔𝗠𝗜, 𝗜𝗝𝗖𝗩, and 𝗜𝗖𝗥𝗔, ensuring that the solutions I deliver are research-validated and industry-ready. 𝗖𝗼𝗿𝗲 𝗘𝘅𝗽𝗲𝗿𝘁𝗶𝘀𝗲 𝗮𝗻𝗱 𝗦𝗲𝗿𝘃𝗶𝗰𝗲𝘀: • Computer Vision, Machine Learning & Deep Learning • YOLO-based Object Detection & Tracking • Real-time Video Analytics & Inference Systems • Medical Imaging AI (CT, MRI, X-ray, Ultrasound) • Healthcare AI & Radiology Diagnostics • OCR, Document AI & Intelligent Data Extraction • AI Automation & Workflow Systems • Drone Analytics, Surveillance & Action Recognition • Image Segmentation, Classification & Feature Extraction • Edge AI & Optimized Model Deployment • Scalable AI Systems & Production ML Pipelines • AI Agents & Intelligent Systems 𝗠𝗼𝗱𝗲𝗹𝘀, 𝗔𝗹𝗴𝗼𝗿𝗶𝘁𝗵𝗺𝘀 𝗮𝗻𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: • YOLO (YOLOv5 – YOLOv10) • Mask R-CNN, U-Net, DeepLabV3, EfficientNet • Custom CNN, GAN, Multi-Modal and Transformer-based vision models • Classical and deep image processing for Computer Vision and Medical Imaging pipelines 𝗧𝗲𝗰𝗵𝗻𝗶𝗰𝗮𝗹 𝗦𝗸𝗶𝗹𝗹𝘀 𝗮𝗻𝗱 𝗧𝗼𝗼𝗹𝘀: 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝗺𝗶𝗻𝗴: Python, MATLAB, C++ 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: PyTorch, TensorFlow, Keras 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 / 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗜𝗺𝗮𝗴𝗶𝗻𝗴: OpenCV, SimpleITK, ITK, Image Processing Pipelines 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Scikit-learn, Pandas, NumPy 𝗢𝗖𝗥: Tesseract, PaddleOCR, EasyOCR 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁: Flask, FastAPI, Docker, REST APIs, Model Serving 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻: Inference Optimization, Model Testing, Scalable AI Pipelines 𝗣𝗿𝗼𝗳𝗲𝘀𝘀𝗶𝗼𝗻𝗮𝗹 𝗘𝘅𝗽𝗲𝗿𝗶𝗲𝗻𝗰𝗲 • Principal Machine Learning Engineer in multi-vehicle tracking & real-time Computer Vision systems • Senior Computer Vision Scientist in surveillance, traffic monitoring, healthcare AI & medical imaging solutions • Contributor to projects funded by IARPA, Facebook, Google, HEC, NIJ, Xerox (USA) & US DTS My work in medical imaging AI has been recognized through Google and Facebook Research Awards, while contributions to sports video analytics, CCTV analytics, and healthcare AI have been acknowledged by HEC and top-tier venues such as CVPR. 𝗪𝗵𝘆 𝗖𝗹𝗶𝗲𝗻𝘁𝘀 𝗖𝗵𝗼𝗼𝘀𝗲 𝗠𝗲 • Strong academia & industry expertise in AI, ML, Computer Vision & Healthcare AI • Experience in medical image processing, blood cancer detection & tumor segmentation • Clean, efficient, and production-ready code • Proven deployment experience in real-world and healthcare systems • Clear communication, dependable delivery, and scalable AI solutions 𝗪𝗵𝗮𝘁 𝗜 𝗖𝗮𝗻 𝗗𝗼 𝗙𝗼𝗿 𝗬𝗼𝘂 • Design custom ML models aligned with business and clinical goals • Deliver accurate, scalable, and reliable AI solutions • Simplify Computer Vision, Medical Imaging, and healthcare workflows • Support deployment and production integration 𝗜𝗳 𝘆𝗼𝘂 𝗮𝗿𝗲 𝗹𝗼𝗼𝗸𝗶𝗻𝗴 𝗳𝗼𝗿 𝗮𝗻 𝗔𝗜/𝗠𝗟 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗖𝗼𝗺𝗽𝘂𝘁𝗲𝗿 𝗩𝗶𝘀𝗶𝗼𝗻 𝗘𝗻𝗴𝗶𝗻𝗲𝗲𝗿, 𝗼𝗿 𝗠𝗲𝗱𝗶𝗰𝗮𝗹 𝗜𝗺𝗮𝗴𝗶𝗻𝗴 𝗦𝗽𝗲𝗰𝗶𝗮𝗹𝗶𝘀𝘁 to build, optimize, and deploy custom AI solutions, I’m available to discuss your requirements and deliver reliable results.