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AI Engineering Lead and PhD researcher in AI/ML, certified in Claude by Anthropic. I design and ship production systems built around Claude: agents, RAG pipelines, and automations that actually make it to deployment Over the past 7 years I've helped more than 20 companies put AI into production across the US, Europe, and the Middle East. I've led engineering teams at startups large and small, and I bring a consistent track record as a high performer on the work I take on I post regularly on Medium, X, and YouTube on the latest in AI, ML, and tech, which keeps me on top of how fast the field moves, with an audience of over 10,000 across platforms. I share this work publicly partly because teaching a thing is the best test of whether you understand it What I build: - RAG chatbots and agents over your documents, PDFs, Notion, and knowledge bases - LLM fine-tuning on your domain data - Workflow automations that replace 40–80% of manual operations - Solution architecture, so you commit to the right stack the first time - Recovery work on stalled or underperforming AI projects Send over the project and I'll reply the same day with a plan, a clarifying question, or an honest pass

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.

Healthcare AI built by an engineer who knows buyers are right to filter out generalists. Medical imaging on DICOM, clinical NLP that handles negation and hedging, HIPAA-aware PHI pipelines, patient-facing apps that respect privacy for hospitals, radiology centres, pharma R&D, CROs, medical device makers, health-tech teams. I work with the Brainy Neurals team as the healthcare-AI engineer. My focus sits between "we have clinical data" and "validated AI helping clinicians" model design, PHI handling under your BAAs, on-prem vs cloud, validation against radiologist ground truth, shipping into EHR or device workflow. WHAT I BUILD / IN HEALTHCARE Medical imaging AI — DICOM ingestion, de-identification per Safe Harbor, organ and lesion segmentation on CT and MRI, abnormality detection, NIfTI and ITK pipelines, 3D Slicer. Built on MONAI, nnU-Net, TotalSegmentator, RadImageNet, validated against radiologist consensus. Clinical NLP and document AI note summarisation, ICD and SNOMED mapping, negation and hedging, family-vs-patient history disambiguation, medication extraction, lab-report parsing, prescription OCR. The grammar of clinical text is its own thing; I treat it accordingly. Pharma and clinical research clinical protocol to eCRF extraction (demographics, vitals, inclusion-exclusion, study-arm), systematic review using RoB2, GRADE, PRISMA, citation validation, batch record digitisation, evidence assembly. Patient-facing apps GI symptom-trackers with AI food and lifestyle recommendations, diabetic and gut-condition dish-suggestion, telehealth onboarding, chronic-care intake, mental-health support. Flutter or React Native fronts, FastAPI backends. Hospital workflow AI appointment automation, triage, hand-hygiene monitoring, fall-detection in wards (pose-based, no face recognition), bed-occupancy. THE STACK / FOR MEDICAL & PHARMA Imaging: MONAI, DICOM, NIfTI, ITK, 3D Slicer, nnU-Net, TotalSegmentator, RadImageNet, modality fusion. Clinical NLP and docs: Docling for medical PDFs, DocTR and TR-OCR for prescription and lab OCR, GPT-4o multimodal for complex layouts, Claude and Gemini for clinical reasoning, regex and Pydantic validation, NegEx-style negation with LLM verification. Knowledge: Neo4j for clinical knowledge graphs (drug-drug interaction, condition-symptom, contraindications). pgvector or Qdrant for medical literature. RAG over institutional protocols, authoritative sources only. Infrastructure: FHIR and HL7 v2 with EHRs, OMOP CDM for research, FastAPI for clinical APIs, Flutter and React Native for patient apps, AWS and Azure inside client BAAs, on-prem Ollama and vLLM where data cannot leave. WHO I BUILD FOR Hospitals and clinics radiology AI, patient-flow analytics, EHR decision support, ward-safety. Radiology centres, DICOM pipelines, organ and lesion segmentation, second-read AI, reporting integration. Pharma R&D and CROs clinical protocol extraction, systematic review, eCRF generation, evidence assembly. Medical device (pre-clearance) model prototyping, validation harnesses, dataset curation. I do not claim FDA-cleared deliverables; I build the substrate that goes through your regulatory team. Telehealth and health-tech patient intake, AI triage, multilingual symptom-checker. Specialty practices dental, dermatology, gastroenterology, oncology, mental health. Specialty-tuned models on small datasets. Health insurance claim review, prior-auth triage, appeals support, evidence extraction. HOW I WORK / WITH HEALTHCARE BUYERS Discovery is a 30-minute call: data sources, clinical workflow, compliance posture (HIPAA, GDPR, local). By the end I tell you whether the use case is feasible, validation plan, where ground truth comes from, how PHI flows. If it touches a regulated device pathway, I tell you what is in scope for me and what your regulatory team owns. Pricing is fixed-scope per milestone feasibility, dataset and PHI design, model build, validation, integration handoff. Hourly only for maintenance after deploy. THE BRAINY NEURALS BACKING I work with the Brainy Neurals team 15 AI engineers, NVIDIA Inception Partner, AWS Activate, Microsoft for Startups. When a project needs ward-monitoring cameras, edge deployment on hospital hardware, RAG over clinical docs, or workflow automation around the AI, that capacity sits with the team I bring them in cleanly, you do not manage multiple vendors. For pure healthcare model and clinical-NLP work I lead end to end myself. LET'S TALK / IF You are inside healthcare or pharma, you have a clinical workflow AI can genuinely help with, you understand the validation and compliance work this requires, and you want a senior engineer who has built medical imaging, clinical NLP, and patient-facing apps before not a generalist learning HIPAA on your project. Tell me your data, workflow, compliance. I reply within 24 hours with feasibility, validation approach, milestones.

I am an AI engineer with 4 years of experience building and deploying production ready AI systems across computer vision, NLP, and generative AI. I have worked on everything clients need right now: fine tuning and training deep learning models, building RAG and GraphRAG pipelines, LLM powered applications, OCR and document extraction, real time face recognition and multi object tracking, classification systems, embedding pipelines, and end to end data workflows from raw input to deployed output. I also have experience with model optimization including distillation, pruning, and quantization for edge and cloud deployment. On the engineering side I am comfortable with FastAPI, PostgreSQL, pgvector, Python async, and cloud and GPU based deployments. I have built and shipped full stack AI products, not just models. I also lead a team of AI engineers, so I understand both deep technical execution and what it takes to deliver consistently on real projects. If you have an AI problem that needs to actually work in production, I can build it.

I'm a senior full-stack engineer with 𝟏𝟓+ 𝐲𝐞𝐚𝐫𝐬 of experience, including nearly a decade building at scale for 𝐀𝐩𝐩𝐥𝐞 and 𝐀𝐦𝐚𝐳𝐨𝐧. Unlike most generalists, I also build production-grade AI features. That combination is rare, and it's why clients hire me. Whether you need a scalable web application, a complex backend system, or AI/LLM capabilities woven into an existing product, I can own it end-to-end, architecture through deployment, frontend through infrastructure. 𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 𝐃𝐞𝐯𝐞𝐥𝐨𝐩𝐦𝐞𝐧𝐭 React + TypeScript frontends with clean component architecture, real-time updates, secure auth flows, and performance tuned for real users. Java backends (Spring Boot, WebFlux, reactive systems), Node.js/TypeScript APIs, REST & GraphQL (Apollo), microservices design, and distributed data layers across DynamoDB, SQL, and NoSQL. I build applications that are testable, maintainable, and designed for scale from day one, not patched together after the fact. 𝐀𝐈 & 𝐋𝐋𝐌 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧 This is where I pull ahead. When a project calls for intelligence, a chatbot, a knowledge assistant, a RAG pipeline, automated workflows, I don't hand it off to a separate AI contractor. I build it myself, integrated cleanly into the same stack. GPT/Gemini integrations, retrieval-augmented generation, context-aware chat systems, prompt pipeline design, structured output with validation, and hallucination mitigation. AI that's engineered into the product, not bolted on. 𝐂𝐥𝐨𝐮𝐝 & 𝐈𝐧𝐟𝐫𝐚𝐬𝐭𝐫𝐮𝐜𝐭𝐮𝐫𝐞 AWS-native architectures, Kubernetes, Helm, Docker, CI/CD pipelines, and production observability with Prometheus, Grafana, and Datadog. SLO-driven monitoring and reliable deployment workflows. Features don't matter if they're not up, I make sure they stay up. 𝐖𝐡𝐲 𝐜𝐥𝐢𝐞𝐧𝐭𝐬 𝐜𝐡𝐨𝐨𝐬𝐞 𝐦𝐞 𝐨𝐯𝐞𝐫 𝐨𝐭𝐡𝐞𝐫 𝐬𝐞𝐧𝐢𝐨𝐫 𝐝𝐞𝐯𝐬:: Most full-stack engineers can't touch AI. Most AI freelancers can't architect real systems. I do both, and I've done it inside two of the most demanding engineering organizations in the world. 𝐒𝐞𝐥𝐞𝐜𝐭𝐞𝐝 𝐫𝐞𝐬𝐮𝐥𝐭𝐬: At Apple, built a React + TypeScript platform for large-scale test execution, developed an AI-powered internal support chatbot, and improved system observability, cutting incident resolution time by 30% and reducing deployment time by 30%. At Amazon, migrated critical systems from Oracle to DynamoDB, reduced infrastructure maintenance by 20%, designed resilient AWS-native services, and helped launch Amazon in new international markets. 𝐈'𝐦 𝐭𝐡𝐞 𝐫𝐢𝐠𝐡𝐭 𝐟𝐢𝐭 𝐢𝐟 𝐲𝐨𝐮 𝐧𝐞𝐞𝐝: A senior engineer who can own your product end-to-end. A full-stack application built properly, architecture-first, scalable, maintainable. AI or LLM features added to an existing product without breaking what's already there. A complex backend, a polished frontend, or both. A technical partner who ships production systems, not prototypes. 𝐇𝐨𝐰 𝐈 𝐰𝐨𝐫𝐤: Architecture-first. MVP fast, production-ready fast. Clean, testable code. Clear communication. Built for scale from day one. I don't build throwaway demos. I build systems that survive production, with or without AI. Let's talk.

AI Architect & Senior ML Engineer with 10+ years of academic + industry experience building production-grade intelligence systems, LLM pipelines, and multimodal AI. I design and deploy end-to-end ML architectures, including LLM orchestration, timeseries modeling, graph ML, OCR pipelines, and automated image-processing systems. Trusted by enterprises for high-reliability AI, data privacy, and scalable ML infrastructure.