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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

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.

Principal AI Engineer and Software Architect specializing in production AI systems: agents, RAG platforms, workflow automation, document intelligence, and full-stack AI applications. I help companies turn complex AI ideas into reliable software products — with the architecture, backend services, APIs, cloud infrastructure, evaluation, observability, security, and deployment practices needed for real business use. My background combines 20+ years of enterprise software engineering with hands-on applied AI delivery across regulated, high-volume environments. I can own both the AI layer and the production system around it. What I build: ▸ AI Agents & Agentic Workflows Custom agents that use tools, call APIs, retrieve data, produce structured outputs, handle multi-step tasks, recover from failures, and integrate with real business workflows. Stack: LangGraph, LangChain, OpenAI, Anthropic Claude, AWS Bedrock, MCP, tool/function calling, memory, state machines, human review, tracing, evaluation, and guardrails. ▸ Production RAG & Knowledge Systems RAG systems for documents, logs, runbooks, tickets, policies, contracts, and internal knowledge. I design ingestion, chunking, embeddings, hybrid search, BM25 + vector retrieval, reranking, citations, grounding, evaluation, and hallucination reduction using pgvector, PostgreSQL, OpenSearch, Pinecone, Weaviate, FastAPI, Claude, OpenAI, and AWS Bedrock. ▸ AI Automation Systems LLM-powered automation for finance, claims, support, operations, research, reporting, document workflows, approvals, and legacy processes. I connect AI to APIs, databases, queues, SaaS tools, internal systems, and human-in-the-loop review. ▸ Full-Stack AI Applications AI assistants, internal copilots, document extraction tools, AI search products, workflow apps, dashboards, SaaS platforms, and customer-facing AI products. ▸ Software Architecture & Backend Engineering Java, Kotlin, Spring Boot, Python, FastAPI, Node.js, TypeScript, React, Next.js, PostgreSQL, MongoDB, Redis, Kafka, REST, GraphQL, microservices, event-driven architecture, system design, and API integrations. ▸ Cloud, DevOps & Production Readiness AWS, AWS Bedrock, Docker, Kubernetes, Terraform, CI/CD, monitoring, logging, cost-aware deployment, security review, observability, and production operations. Selected production experience: • Built production RAG with hybrid BM25 + vector retrieval, reciprocal rank fusion, reranking, citation-grounded streaming, and a CI-gated evaluation harness that improved faithfulness from 0.81 to 0.95. • Built LangGraph agents with explicit state machines, conditional replanning, validated tools, Postgres checkpointing, LangSmith tracing, and resumable execution after process failure. • Built MCP servers exposing typed tools for sandboxed SQL, HTTP/GraphQL, and semantic search across stdio, HTTP, and SSE. • Led AI-driven automation for high-volume claims workflows, including ingestion, validation, transformation, exception handling, and LLM-assisted legacy rule extraction. • Built agent-based transaction analysis using REST, Splunk, and AI agents for real-time financial data; finalist in a Finance Technology AI Hackathon. • Architected enterprise backend and data platforms using Java/Spring Boot, Kafka, PostgreSQL, Oracle, AWS/Azure, Terraform, Docker, Kubernetes, CI/CD, logging, tracing, and production runbooks. • Led delivery across financial services, healthcare, retail, hospitality, publishing, and technology clients, including distributed teams up to 55 engineers and multi-vendor integrations. Why clients hire me: • I can own the full system: AI workflow, backend, frontend, database, cloud, and deployment. • I design for reliability, evaluation, observability, security, cost control, and maintainability. • I understand regulated environments, PII, audit trails, compliance constraints, and production risk. • I can operate as hands-on builder, architect, technical lead, and stakeholder-facing delivery partner. Core stack: AI Agents, RAG, LLM Apps, LangGraph, LangChain, MCP, OpenAI, Anthropic Claude, AWS Bedrock, Tool Calling, Function Calling, LLM Evaluation, RAGAS, LangSmith, pgvector, Pinecone, Weaviate, OpenSearch, Python, FastAPI, Java, Kotlin, Spring Boot, Node.js, TypeScript, React, Next.js, PostgreSQL, MongoDB, Redis, Kafka, AWS, Docker, Kubernetes, Terraform, CI/CD. Send me a short description of what you want to build, what systems/data it needs to connect to, and what outcome matters. I’ll help define the architecture, risks, milestones, and practical path to production.

𝗦𝗰𝗮𝗹𝗲 | 𝗠𝗶𝗴𝗿𝗮𝘁𝗲 | 𝗗𝗲𝗽𝗹𝗼𝘆 You have a visionary SaaS or AI product idea. You don't want to manage a scattered team of backend developers, DevOps engineers, and ML specialists, and you definitely don't want to micromanage tasks. You need a single, accountable technical partner to architect the system, solve the hard problems, and turn your idea into a scalable reality. I’m a Computer Science grad (first-class), but I don't just live in the theory. I combine a heavy academic background in math, stats, and ML with fast, practical execution. I don't just write code. I take ownership of the whole project: the design, the architecture, and the infrastructure. 🧠 𝗛𝗼𝘄 𝗜 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵 𝗬𝗼𝘂𝗿 𝗦𝘆𝘀𝘁𝗲𝗺: When you hand me a prototype, an MVP, or even just a wireframe, my first step is mapping the architecture. I am a natural problem solver, so I look at the big picture first. I treat your product like a business asset and focus on the hard realities: 🔹 Can we launch the MVP faster to validate the market? 🔹 Does it do exactly what your users need? 🔹 Is the AWS bill optimized, or are we burning money? 🔹 Will it crash if traffic spikes tomorrow? 🔹 Is your proprietary data actually secure? 🔹 If a server dies, is the database instantly recoverable? 📈 𝗥𝗲𝗰𝗲𝗻𝘁 𝗪𝗶𝗻𝘀 & 𝗪𝗵𝗮𝘁 𝗜 𝗗𝗲𝗹𝗶𝘃𝗲𝗿: 🔹 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 𝗦𝗰𝗮𝗹𝗶𝗻𝗴 & 𝗖𝗼𝘀𝘁 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Recently, completely re-architected a system to handle 𝟰𝟬𝘅 𝗰𝗼𝗻𝗰𝘂𝗿𝗿𝗲𝗻𝘁 𝗽𝗿𝗼𝗰𝗲𝘀𝘀𝗶𝗻𝗴 while achieving a 𝟳𝟬% 𝗰𝗼𝘀𝘁 𝗼𝗽𝘁𝗶𝗺𝗶𝘇𝗮𝘁𝗶𝗼𝗻 on cloud spending. 🔹 𝗔𝗜-𝗡𝗮𝘁𝗶𝘃𝗲 𝗦𝗮𝗮𝗦 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲: I use my deep learning research experience to build secure, private LLM and RAG pipelines tailored to your business data. 🔹 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲𝗱 𝗖𝗹𝗼𝘂𝗱 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 (𝗗𝗲𝘃𝗢𝗽𝘀): I architect zero-downtime CI/CD pipelines and configure AWS/GCP environments so you can ship features fast. 🔹 𝗕𝘂𝗹𝗹𝗲𝘁𝗽𝗿𝗼𝗼𝗳 𝗕𝗮𝗰𝗸𝗲𝗻𝗱 𝗦𝘆𝘀𝘁𝗲𝗺𝘀: I engineer high-performance backends (Django, Node) that won't crash when your user base scales. 🤝𝗠𝘆 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝗦𝘁𝘆𝗹𝗲: I work best with founders and small teams who need a leader. You bring the business problem and the priorities — what matters next and why. I own the rest: the architecture, how the work gets broken down, and the delivery. I run it in focused sprints against agreed goals, with async updates and a call when a real decision needs one — so you get momentum without daily standups eating your week. When you hire me, you are not buying hours of typing, or a developer to assign tickets to. You are buying the judgment, speed, and technical leadership required to win — and a clean, production-ready product handed back to you. Let's look at your system and get it running right.

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.

🚀 🥇 Expert-Vetted Talent | Principal AI/ML | GenAI, LLM, RAG, Fine-tuning, Cloud(Azure, AWS, GCP) | Healthcare & FinTech I transform complex business challenges into cutting-edge AI solutions. With 15+ years leading AI/ML initiatives and a Doctor of Philosophy in Machine Learning from Iowa State University, Master's in Computational Neuroscience from UC San Diego, I deliver production-ready GenAI systems that drive measurable ROI. ✅ What I Offer: • GenAI & LLM Solutions: Custom GPT fine-tuning, prompt engineering, multi-agent systems, RAG architectures • Production AI Systems: End-to-end ML pipelines, MLOps, model deployment at enterprise scale • Healthcare AI: Clinical trial automation, medical document generation, HIPAA-compliant solutions • AI Strategy & Architecture: Technical roadmapping, proof-of-concepts, AI transformation leadership • Full-Stack AI Development: Python, PyTorch, TensorFlow, AWS, GCP, Azure, Vector DBs, Kubernetes 🎯 Recent Achievements: • Built AI system processing 100K+ clinical trials for pharma enterprise, enabling intelligent Q&A and benchmarking • Developed GenAI platform generating 100+ page clinical trial protocols with 95% accuracy • Led 60+ engineer team delivering first commercial AI healthcare product at ResMed (NYSE: RMD) • Created $1M+ revenue AI products in first year at multiple startups • Secured $3.5M+ funding through strategic AI product development 💼 Industry Expertise: • Healthcare/Pharma: Clinical trials, EHR integration, medical AI, FDA-regulated software • FinTech: Real-time fraud detection, risk assessment, payment processing systems • Enterprise SaaS: Multi-tenant architectures, API development, scalable AI services • Retail/E-commerce: Recommendation engines, inventory optimization, customer analytics 🔧 Technical Stack: AI/ML: GPT-4/5, Claude, Llama, BERT, Transformers, Fine-tuning, RLHF, RAG, Vector Databases Languages: Python, JavaScript, TypeScript, Java, Ruby, Go, SQL Frameworks: PyTorch, TensorFlow, Hugging Face, LangChain, Scikit-learn, FastAPI Cloud/DevOps: AWS (SageMaker, Lambda, ECS), GCP (Vertex AI), Azure, Kubernetes, Docker Databases: PostgreSQL, MongoDB, Redis, Elasticsearch, Pinecone, Weaviate, ChromaDB Tools: MLflow, Weights & Biases, Git, CI/CD, Jupyter, Streamlit 📊 Quantifiable Impact: • 73% engagement increase for nonprofit youth chatbot with emotional intelligence • 94% accuracy in crisis detection for at-risk populations • 10X growth in subscriber base through AI-driven optimizations • 100+ pages of medical documents generated with regulatory compliance • $100K+/month revenue generated through ML-powered ad targeting 🎓 Credentials: • M.Sci. Computational Neuroscience - UC San Diego • IBM Certified: RAG and Agentic AI • Deep Learning Specialization - Coursera • Published researcher in AI/ML (Psychological Science, ICSE) • 4 provisional patents in AI/computer vision 🌟 What Sets Me Apart: Unlike typical developers, I combine deep technical expertise with business acumen from leading multiple successful startups. I don't just build models – I architect complete AI ecosystems that scale, comply with regulations, and deliver measurable business value. My neuroscience background provides unique insights into building truly intelligent systems that understand human behavior and needs. 📋 Services I Provide: • Custom LLM fine-tuning and deployment • RAG system architecture and implementation • Multi-agent AI system development • Healthcare AI and medical document automation • AI-powered data extraction and processing • Computer vision and image analysis solutions • Production ML pipeline development • AI strategy consulting and roadmapping • Technical due diligence for AI projects • Team mentoring and AI capability building 🤝 Working With Me: I pride myself on clear communication, translating complex AI concepts into actionable business strategies. Whether you need a production-ready AI system, strategic guidance, or technical leadership, I deliver solutions that exceed expectations. I'm available for both short-term projects and long-term engagements. Ready to transform your business with AI? Let's discuss how I can help you leverage the latest in GenAI, LLMs, and machine learning to achieve your goals. Keywords: Artificial Intelligence, Machine Learning, Deep Learning, GenAI, Generative AI, ChatGPT, GPT-4, GPT-5, Large Language Models, LLM, Fine-tuning, RAG, Retrieval Augmented Generation, RLHF, Vector Database, Embeddings, Transformers, NLP, Computer Vision, PyTorch, TensorFlow, Python, AWS, GCP, Azure, Healthcare AI, FinTech AI, MLOps, AI Architecture, Prompt Engineering, LangChain, Hugging Face