Hire the Best Computer Vision Engineers
in Pakistan

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.

Expert-Vetted Top 1% Computer Vision Engineer with 11+ years deploying production-grade Computer Vision, Medical Imaging & Healthcare AI, Multimodal RAG, Edge AI, and MLOps systems — trusted by Fortune 500 and global enterprises, including P&G, Edwards Lifesciences, Canva, and Shotdeck. Featured talent at Upwork Executive Summit. Fully async with EU and US morning overlap (ET). The common thread across 80+ production deployments is a single architectural principle: isolate the binding constraint before any model decision — HIPAA compliance in clinical pipelines, sub-second retrieval across 3M+ cinematic assets, compute efficiency at 2M+ API calls/year, real-time inference on resource-constrained edge hardware. I own the full stack from data strategy through deployment: design, training, optimization, and production maintenance. My hands-on execution spans five specialized, production-proven domains: ◆ COMPUTER VISION (11 years in production) Built Shelfr's complete retail shelf-intelligence platform from zero — SKU detection, planogram compliance, OOS recognition, share-of-shelf — scaling to 200+ global FMCG brands including P&G. Awarded the 2025 P&G External Business Partner Excellence Award. Built face anti-spoofing and biometric verification systems at FOO Technologies — 90%+ accuracy against video replay, printed face, and mask attacks; document tampering detection at 91%. Deep specialization: YOLO v5–v11 · YOLO-World · Grounding DINO · SAM / SAM 2 · Vision Transformer (ViT · Swin) · ByteTrack · OpenCV · instance & semantic segmentation · object detection & tracking · face recognition & anti-spoofing · pose estimation · few-shot & zero-shot learning · anomaly detection. ◆ HEALTHCARE AI & MEDICAL IMAGING (HIPAA · DICOM · FHIR · Clinical NLP) Delivered production clinical AI for Edwards Lifesciences — HIPAA-compliant DICOM de-identification via custom U-Net, echocardiogram analysis, beat-to-beat LVEF assessment, and Attention-Based MIL for weakly labeled studies. Production medical NLP achieving 85%+ PHI detection accuracy across clinical notes, FHIR documents, and lab tables — context-aware obfuscation preserving clinical utility while eliminating re-identification risk. Late-interaction retrieval (ColPali/ColBERT) for radiology and pathology records in regulated environments. Deep specialization: MONAI · DICOM · FHIR · nnU-Net · MedSAM · PHI de-identification · BioBERT · ClinicalBERT · medical image segmentation · biomedical NLP · EHR integration · HIPAA/GDPR compliance. ◆ MULTIMODAL AI & CROSS-MODAL RETRIEVAL Architected Shotdeck's production retrieval system across the world's largest cinematic library — 3M+ assets — delivering sub-second natural-language queries via hybrid pgvector retrieval with HNSW indexing on 100+ parallel GPUs. Built any-modality search supporting dialogue, reference images, ambient audio, and video clip inputs — with 76 structured cinematographic tags across 15 categories. Deep specialization: CLIP · DINOv2 · LLaVA · Qwen VLMs · Florence-2 · ImageBind · ColPali · ColBERT · Whisper · multi-vector retrieval · late-interaction token-level matching · image-text-audio-video embeddings. ◆ GENERATIVE AI · RAG & AGENTIC SYSTEMS Built the full generative AI stack at Synthesys AI Studio — talking avatars, AI lipsync, voice cloning, text-to-image — scaled to 2M+ API calls/year; reduced compute costs from $100K+/year to a fraction via serverless architecture (Modal, Replicate). AI Humans product integrated into Canva. Production RAG and agentic systems at Biscuit AI: hybrid search, multi-vector reranking, HyDE, LangGraph state machines, CrewAI and AutoGen multi-agent orchestration, long-term memory (semantic, episodic, procedural), LangSmith evaluation and hallucination monitoring. Deep specialization: GPT-4o · Anthropic Claude · Llama 3 · Mistral · Gemini · DeepSeek · LangChain · LangGraph · LangSmith · CrewAI · AutoGen · MCP · function calling & tool use · RAG · agentic RAG · LoRA/QLoRA fine-tuning. ◆ EDGE AI & EMBEDDED DEPLOYMENT Deployed real-time inference across forklift safety and construction monitoring (Canaryaware), ATM anti-skimming, and multi-animal behavioral tracking — QAT-optimized architectures maintaining low-latency detection under industrial conditions (low light, occlusion, motion blur) within edge-cloud hybrid systems with centralized continuous learning. Deep specialization: NVIDIA Jetson (Nano · TX1 · TX2 · Xavier · Orin) · Google Coral TPU · Raspberry Pi · Intel NPU / OpenVINO · TensorRT · ONNX Runtime · LiteRT · INT8/QAT quantization · model pruning · TinyML. Building a medical imaging platform, multimodal retrieval product, production RAG system, or edge deployment? Message me with your use case or the bottleneck you're hitting — I respond within 24 hours and can typically set a discovery call within 48 to map your fastest path to production.

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!!

I am a Computer Vision Engineer with 6 years of experience building real-world AI systems for images, videos, live cameras, documents, and automation workflows. I help clients turn raw visual data into working AI solutions — including object detection, object tracking, segmentation, OCR, pose estimation, face recognition, ANPR, people counting, defect detection, medical image analysis, drone/aerial analysis, and production deployment. I am a Top Rated Plus freelancer with 100% Job Success, 1,800+ Upwork hours, and 5-star client feedback. I do not just train models — I build complete, production-ready systems. My core expertise: • Object Detection & YOLO Custom YOLOv5, YOLOv8, YOLOv9, and YOLOv10 model training for people, vehicles, PPE, products, defects, sports, agriculture, surveillance, and custom objects. • Object Tracking & Video Analytics Real-time tracking using OpenCV, ByteTrack, DeepSORT, StrongSORT, line crossing, zone counting, people counting, vehicle counting, heatmaps, crowd monitoring, and movement analysis. • Live Camera, CCTV & RTSP AI AI pipelines for CCTV, RTSP streams, webcams, drones, surveillance systems, industrial cameras, and real-time detection dashboards. • OCR, ANPR & Document AI Text extraction, number plate recognition, invoice/receipt processing, ID card extraction, table extraction, and document automation using Tesseract, EasyOCR, PaddleOCR, AWS Textract, and custom preprocessing. • Segmentation, Pose & Face AI Image segmentation, pose estimation, action recognition, face recognition, emotion detection, biometric verification, and human activity analysis. • Deployment & Production Systems FastAPI, Flask, Docker, AWS EC2, S3, Lambda, SageMaker, DynamoDB, APIs, dashboards, and cloud-based AI deployment. What I can deliver: ✔ Custom computer vision models ✔ YOLO object detection systems ✔ Real-time tracking and counting pipelines ✔ CCTV/RTSP/webcam AI integration ✔ OCR and ANPR automation ✔ Image segmentation and pose estimation ✔ Face recognition and video analytics ✔ Dataset preparation and annotation guidance ✔ API, dashboard, and cloud deployment ✔ Clean, documented, maintainable code Why clients choose me: ✔ 6 years of computer vision experience ✔ Top Rated Plus freelancer ✔ 100% Job Success Score ✔ 1,800+ Upwork hours ✔ 5-star client feedback ✔ End-to-end delivery: dataset → model → API → deployment ✔ Fast communication and daily progress updates ✔ Production-focused solutions that work in real environments If you need a reliable computer vision engineer to build, fix, improve, or deploy an AI system, send me your project details and I will respond with a clear technical plan.

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.