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I’m Niloy, an AI engineer specialising in computer vision, deep learning, generative AI and full-stack ML systems. I build high-performance, production-ready AI solutions for companies that need reliable results, not experiments. I work across the full lifecycle: scoping requirements, architecting systems, preparing datasets, training and fine-tuning models, and deploying to scalable cloud infrastructure. My recent work includes building multi-agent systems, HIPAA-compliant AI chatbots, end-to-end computer vision platforms, OCR pipelines for real-world documents, and fully automated systems. 🔧 What I Do • Develop and deploy deep-learning systems: classification, detection, segmentation, retrieval, embeddings, video analytics • Implement RAG systems, custom tools, and hybrid search architectures • Build voice/telephony AI agents for customer service, intake, and automation • Build and fine-tune LLMs, VLMs, multimodal pipelines, and agent-based AI systems • Train OCR models for real-world documents, ID cards, license plates, handwritten text and noisy images • Create synthetic data pipelines and data-augmentation workflows • Design scalable ML backends with FastAPI, AWS/GCP, Docker, CI/CD • Integrate lightweight robotics/embedded CV pipelines when needed 🧰 Technical Stack • Python, PyTorch, TensorFlow, Sklearn, HuggingFace, OpenCV, LangChain/LangGraph, Pipecat • FastAPI, Docker, Kubernetes, GitHub Actions, AWS, GCP, DigitalOcean, Slurm • Postgres, SQL, Pandas, NumPy • Experience with Jetson, Raspberry Pi, ROS2 (for vision-edge use cases) ⭐Selected Work Highlights • Architected a HIPAA-compliant multi-agent system for insurance claims processing using FastAPI, LangGraph and AWS Bedrock • Built vision-based self-checkout (VisionPOS) for low-resource pharmacies; reached 95 percent accuracy in real pilots • Developed multiple production OCR pipelines, including license-plate recognition, ID document extraction, handwritten prescription OCR, and satellite-image super-resolution • Delivered AI voice agents for sales, intake, data entry and customer service integrated with telephony systems • Built reverse image/video search systems and large-scale embedding pipelines • Trained deep learning models for medical imaging and clinical decision support ⚙️How I Work • Clear requirements and structured communication • Reproducible, well-documented code with version control • Milestone-driven delivery • Production mindset: reliability, latency, safety, and maintainability first

🔧 Electrical Engineer | AI/ML Enthusiast | Director Embedded 🌐 Greetings! I am Salman Nawaz Malik, an accomplished Electrical Engineer with a strong foundation from NUST and over four years of hands-on experience in the dynamic realms of technology. My diverse expertise encompasses major programming languages, AWS, embedded systems, power systems, and cutting-edge technologies like AI, ML, and Computer Vision. 🚀 Professional Journey: 🎓 Education: Msc Artificial Intelligence from University of Huddersfield. BSc Electrical Engineering from NUST, where I honed my technical skills and gained a robust understanding of engineering principles. 🌐 Freelance Experience: Successfully delivered multinational-scaled projects as a freelancer, showcasing my adaptability and ability to excel in varied environments. 🛠️ Technical Proficiency: Programming Languages: Proficient in C, C++, Python, R, Java, Assembly Language, Verilog, VHDL (VHSIC Hardware Description Language), SystemVerilog, SystemVerilog, HTML/CSS, JavaScript, Ruby, PHP, Swift, Matlab, Dart and Groovy. AWS: Extensive experience in leveraging AWS services for scalable and efficient solutions. Embedded Systems: Expertise in designing and implementing embedded systems for diverse applications especially in DsPic's and FPGA's. AI, ML, and CV: Applied my passion for artificial intelligence, machine learning, and computer vision to create innovative solutions. 👨‍💻 Professional Experiences: Director Embedded Systems at MiniByte (12/2023 - 07/2024) AI Engineer at AIOBIC pvt ltd (06/2023 - 12/2023) Internee at Care pvt ltd (06/2022 - 09/2022) Internee at Renzyme (06/2021 - 10/2021) 🤝 Let's Connect: I am open to networking, collaboration, and discussions on all things tech! Feel free to connect, and let's explore ways to create, innovate, and elevate the tech landscape together. #ElectricalEngineer #AI #ML #EmbeddedSystems #AWS #Innovation #TechnologyEnthusiast

▅ ▆ ▇ █🗨️𝐌𝐞𝐬𝐬𝐚𝐠𝐞 𝐦𝐞 𝐝𝐢𝐫𝐞𝐜𝐭𝐥𝐲!🗨️█ ▇ ▆ ▅ 🚀Senior Computer Vision & Real-Time AI Engineer | RTSP, CCTV, Edge AI🚀 → Senior AI Engineer with 8+ years of hands-on experience building real-time computer vision systems → Specialized in RTSP, CCTV, IP cameras, and live video analytics for production environments → Strong background in object detection, tracking, OCR, and video intelligence → Delivered end-to-end AI solutions used in security, retail, traffic, and industrial monitoring → Experience building mobile apps for live camera viewing, alerts, and AI event monitoring ## Core Expertise in Real-Time Computer Vision • Object Detection & Tracking: YOLOv8–YOLOv10, DeepSORT, ByteTrack, SORT • Real-Time Video Analytics: CCTV analytics, intrusion detection, object counting, anomaly detection • RTSP & Live Streams: Multi-camera RTSP pipelines, IP cameras, CCTV, drones • OCR & Video Text Extraction: License plate recognition, IDs, invoices, on-video OCR • Edge & Low-Latency AI: NVIDIA GPU, Jetson, edge inference optimization • Camera-Based Monitoring: PPE detection, safety compliance, zone-based alerts ## AI Models & Computer Vision Techniques • Object detection, tracking, and re-identification • Motion analysis and behavior detection • Computer vision–based automation workflows • Real-time inference optimization • Image preprocessing and video frame handling ## Tools, Frameworks & Platforms • Frameworks: PyTorch, TensorFlow, Ultralytics YOLO • CV Libraries: OpenCV, FFmpeg, GStreamer • Tracking: DeepSORT, ByteTrack • APIs & Services: FastAPI, Flask • Deployment: Docker, NVIDIA GPUs, Jetson, AWS EC2 • Languages: Python, C++, JavaScript ## Mobile & Full-Stack Integration • Mobile app development using Flutter and React Native • Live camera viewing and RTSP stream playback on mobile • Real-time AI alerts and notifications • Backend APIs for video metadata and detection results • Dashboards for monitoring AI events and analytics ## Featured Computer Vision Projects - Real-Time CCTV Object Detection System Built a YOLO-based system to detect people, vehicles, and restricted-zone intrusions from RTSP camera feeds. - License Plate Recognition (ANPR) from Video Implemented real-time license plate detection and OCR for parking and traffic automation. - Multi-Camera RTSP Analytics Platform Developed a scalable pipeline processing multiple RTSP streams with tracking and event alerts. - PPE & Safety Monitoring System Built a real-time vision system to detect helmets and safety gear compliance from CCTV footage. - Mobile Camera Monitoring App Created a mobile app for live camera viewing, AI alerts, and event history visualization. ## How I Work • Production-first mindset — not research demos • Focus on performance, accuracy, and stability • Clean, optimized, well-documented code • Clear communication and fast iteration • End-to-end delivery: model → API → deployment → monitoring ## Why Clients Choose Me • Deep experience with real-time video & RTSP pipelines • Proven delivery of production-ready AI systems • Strong combination of computer vision + backend + mobile • Reliable, transparent, and easy to work with • Long-term support and system optimization ## Let’s Work Together If you’re looking for a Computer Vision engineer who can build real-time camera detection, CCTV analytics, or RTSP-based AI systems, feel free to send a message or invite me to your project. I’m happy to review your setup and recommend the best technical approach. Best Regards, Simon A.