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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

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.

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 a AI specialist and a problem-solver. I am expert in using Python to develop effective machine learning models. I have experience with AWS and Tensorflow and have built applications, models, and solutions for a variety of companies. I have developed cutting edge, custom machine learning models and successfully integrated them within existing structures. My goal is to revolutionize your business. I believe that machine learning and AI is the path forward, and I can help you obtain value from the data you collect. If you want to innovation and creativity in your business, reach out to me.

Need to further optimize your LLM/GPT/RAG application performance with the support of a top LLM expert, or start from scratch? 6+ Years, AWS and Microsoft Azure Certified AI/ML Specialist, I am focused on building Advanced AI applications around Large Language models (GPT, Claude, Llama, Gemini, Azure OpenAI, etc.), and have compiled some of the most cutting-edge features in AI, RAG, and NLP to cater to specific domains and complex applications. My process to future-proof your business involves, 1. Setting up your data in the best possible format. 2. Optimizing your AI infrastructure for a more deterministic (yet creative) flow in the probabilistic domain of AI. 3. Developing a scalable architecture that anticipates growth. 4. Delivering cost-effective solutions and long-term support. 5. Prioritizing Data Security and Compliance at each increment. As a contributor to Langchain, LlamaIndex, LangGraph, n8n, and other supporting frameworks, I've significantly engaged with the open-source LLM/RAG library space and continually participate in the LLM/AI community. My Expertise lies in custom AI Agents, Chatbots, Voice AI Agents, and Workflow Automations to make your business/product 10x more efficient. 🎓 Advanced proficiency in the following domains, ✦ Model Deployment ✦ Model Fine-tuning ✦ Knowledge Extraction ✦ Structured Data Query (Connect your database with LLMs) ✦ AI Voice/Speech Agents ✦ MCP & A2A (Connect your APIs and AI Agents) ✦ GraphRAG ✦ RAG Enhancement ✦ Prompt Engineering ✦ Long-term Memory and Continual Learning (For an LLM/AI, context is gold) ✦ LLM Performance Analysis (Evaluation, Governance, and Monitoring) 🔧 𝗖𝗼𝗿𝗲 𝗖𝗼𝗺𝗽𝗲𝘁𝗲𝗻𝗰𝗶𝗲𝘀 ● AWSxAzure Administrator & Solutions Architect - Prioritizing security and compliance, we have built many Azure-only AI Solutions for medium to large-scale enterprises. ● ChatGPT/OpenAI-Based RAG Applications ● LLMOps (Architecture/Development): - Langchain, LlamaIndex, Haystack, AdalFlow, GraphRAG, LangGraph - Cohere, Ollama, Anyscale, Groq, Deepspeed, SentenceTransformer, PyTorch - Any vector database (Pinecone, ChromaDB, Milvus, Qdrant, Weaviate, Azure Search) - AWS SageMaker, Amazon Bedrock, Amazon Q, Azure OpenAI & AI Studio, - Training & Fine-tuning: DPO, RLHF, PPO, PEFT (LoRA, QLoRA) ● Text-to-speech and Speech-to-text: OpenAI Whisper, Google TTS, ElevenLabs, Murf AI, Deepgram, Amazon Polly, Azure TTS ● Python Backend (Flask, Django, FastAPI, Streamlit, Dash, PyQT & Tkinter for GUI) ● Secondary Languages: C#, JavaScript/Typescript, Rust. ● Datastores: MongoDB, PostgreSQL, Redis, DynamoDB, Elasticsearch, Azure CosmosDB ● Linux Server Administration (Amazon-Linux, Debian-based, RHEL), ● Web Servers: Apache2, Nginx ● Containerization and Orchestration: Docker, Docker Swarm, ECS, Kubernetes (Across major Cloud Vendors) ● AWS Serverless: API Gateway, Lambda, SNS, CloudWatch, CloudFront, S3, DynamoDB ● IaC: Cloudformation, Terraform, Ansible ● CI/CD: Jenkins, GitHub Actions, AWS CodeDeploy/CodeBuild, GitLab. ● CRM Integrations: Zendesk, GoHighLevel, HubSpot, Bitrix24. ● Integration and Automation: n8n, Make.com, Zapier, etc. ● Real-Time Data and API Integration 💼 Some mainstream industries and business domains that I have worked with, ✦ CRMs ✦ Healthcare ✦ Operations ✦ Education, Edtech ✦ Legal (Regulation, Compliance, Consultancy) ✦ Real Estate ✦ Retail & E-commerce ✦ Travelling and Hospitality If you are trying to build an MVP for an AI Project, my guide to you would be, "The best way to start is just by building on top of... whatever the best model is... Don’t worry about [cost or latency] at first. You’re just trying to validate the idea." Today, generating and structuring great ideas is easier than ever. What truly sets teams apart now is execution. I’ve developed a robust framework with my experience that enables rapid design and development of AI-first applications, turning ideas into impact with speed and precision. Let's transform customer experience, unlock data intelligence, and solve complex business problems together. Book my consultation if you're new to the concept and want to save several hours searching for the right direction.

🚀Hi! I help startups go from demo → market-ready AI product - fast! I plug into your team and ship production ready features that customers love. 🏆 Top 1% (Expert-vetted) | 8+ years| 100% JSS | 35+ Delighted Clients | 18+ E2E Deployments 🎯 "𝘒𝘶𝘯𝘫𝘢𝘯 𝘪𝘴 𝘢𝘯 𝘢𝘣𝘴𝘰𝘭𝘶𝘵𝘦 𝘨𝘦𝘮! 𝘚𝘩𝘦 𝘸𝘦𝘯𝘵 𝘢𝘣𝘰𝘷𝘦 𝘢𝘯𝘥 𝘣𝘦𝘺𝘰𝘯𝘥. 𝘛𝘦𝘢𝘮𝘪𝘯𝘨 𝘶𝘱 𝘸𝘪𝘵𝘩 𝘩𝘦𝘳, 𝘸𝘦 𝘣𝘶𝘪𝘭𝘵 𝘳𝘰𝘣𝘶𝘴𝘵 𝘥𝘢𝘵𝘢 𝘱𝘪𝘱𝘦𝘭𝘪𝘯𝘦𝘴, 𝘰𝘱𝘵𝘪𝘮𝘪𝘻𝘦𝘥 𝘓𝘓𝘔𝘖𝘱𝘴, 𝘵𝘳𝘢𝘯𝘴𝘪𝘵𝘪𝘰𝘯𝘦𝘥 𝘧𝘳𝘰𝘮 𝘧𝘢𝘯𝘤𝘺 𝘥𝘦𝘮𝘰𝘴 𝘵𝘰 𝘢 𝘴𝘤𝘢𝘭𝘢𝘣𝘭𝘦 𝘈𝘐 𝘱𝘳𝘰𝘥𝘶𝘤𝘵 𝘵𝘩𝘢𝘵 1000𝘴 𝘰𝘧 𝘤𝘶𝘴𝘵𝘰𝘮𝘦𝘳𝘴 𝘭𝘰𝘷𝘦!" - CTO, Healthcare startup, SF (1 of many 5-star reviews) Over 7+ years on Upwork, I've helped startups and enterprises across sectors - Marketing, Healthcare, Law, Education, and Agribusiness - translate AI into measurable business ROI by building the right data and ML foundations. Whether collaborating with 1000+ employee companies or lean 2-person startups, I adapt to the team’s needs: reporting directly to CEOs or CTOs as an AI/Data Lead, or integrating as a hands-on contributor alongside in-house developers. I bring a “brain-for-hire” + hands-on mindset - tackling strategic and technical challenges with equal enthusiasm, always focused on delivering outcomes, not overhead. 👀 See 8+ Case Studies & Demos: totemxlabs dot com/#casestudies 🎯 Recent Success Stories (2024-2025) 1. Graph RAG for Marketing & Strategic decisions Implemented knowledge graph-powered RAG systems using Neo4j for marketing intelligence and SEO optimization - enabling sophisticated knowledge checks and relationship-based insights that traditional RAG cannot achieve. 2. Workplace Safety Vision System Deployed end-to-end computer vision solution for workplace health and safety compliance - real-time hazard detection, PPE verification, and automated incident reporting. 3. Healthcare Diagnostic Knowledge Graph Developed evidence-based diagnostic system using knowledge graphs for healthcare - producing auditable, traceable diagnostic reasoning paths that meet clinical documentation standards. 🧭 My Proven AI Product Process: Discovery: Identify high-ROI AI opportunities aligned with your goals. Data Assessment: Evaluate data availability, relevance, quality. Solution Design: Define user journeys, tech stacks, evaluation frameworks. Development: Optimized sprints for cost-effective, robust AI solutions (min. tech debt). Deployment: Scalable architectures, rigorous testing for production readiness. 💡 Technical Expertise: GenAI & LLMs: LLM Ops (Fine-tuning, Prompt Engineering, RAG, Agentic Workflow, Multi-agent systems, Memory Management), LangChain, LangGraph, LlamaIndex, LangServe; Vector Search (Pinecone, Weaviate, Chroma, FAISS). AI/ML & MLOps: Predictive Analytics, Recommender Systems, RLHF; Model Lifecycle, MLflow, Feature Stores; PyTorch, TensorFlow, Scikit-learn. Cloud & Infra (Multi-cloud): AWS (Lambda, ECS, ElastiCache, DynamoDB, API Gateway), Azure, GCP, Vertex AI, SageMaker; Docker, Kubernetes (EKS/GKE). Software & Data Eng: Python (expert), Fullstack; FastAPI, Flask, Django; REST, GraphQL, OpenAPI, AsyncIO; Batch/Stream (Airflow, Spark, Kafka), Pandas, Polars, dbt; ETL/ELT. DBs/Arch/DevOps: PostgreSQL, MySQL, DynamoDB, MongoDB, Vector DBs (Pinecone, Weaviate, ChromaDB), Neo4j; Microservices, GitHub Actions, Terraform, CI/CD pipelines. 🤝 Why Partner With Me? I help architect and develop E2E AI solutions merging tech excellence with business acumen. Let's transform the AI wave into your competitive edge. ✅DM me if you have an exciting problem to solve! —Updated January, 2026— Latest LLMs (2025-26): GPT-5.2 (OpenAI's flagship with 26% lower hallucination rate) Claude 4.5 Opus & Sonnet 4.5 (200K context, breakthrough coding performance) Gemini 3 Pro & Flash (Deep Think mode, multimodal excellence, 1M token context) DeepSeek V3.2 & R1 series (685B parameters, hybrid thinking/non-thinking modes) Qwen 3 series (open weight dense and mixture-of-experts (MoE) model for reasoning tasks) Agentic AI Frameworks: Langchain & LangGraph (stateful graph-based workflows, production-grade orchestration) OpenAI Agents SDK (lightweight with guardrails and handoffs) CrewAI (role-based multi-agent collaboration, human-in-the-loop) LlamaIndex Agents (RAG-first agent capabilities) AI workflow automation: n8n Knowledge Graphs & Graph RAG: Neo4j 2025 features: Community summaries, parallel retrievers, LLM Knowledge Graph Builder Neo4j Aura Graph Analytics Graph RAG architecture Cypher query optimization, graph embeddings for ML Agentic knowledge graph construction with provenance Computer Vision Applications: Workplace safety, Video generation Multimodal Vision Language Models (VLMs) Context engineering for high quality content generation