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I build production AI systems — multi-agent pipelines, RAG architectures, and LLM-powered automation — plus the full-stack platforms (React, Node.js, AWS) that ship them to real users. Over the past 6 years I've gone from full-stack engineer to AI engineer, and I bring both skill sets to every project: I can design the agent architecture AND build the production-grade app around it. WHAT I DO - Agentic AI systems — multi-agent pipelines with LangGraph, LangChain, CrewAI, and the OpenAI Agents SDK - RAG pipelines — hybrid retrieval (vector + keyword search), relevance filtering, and grounded/verified generation - LLM integrations — OpenAI, Bedrock, and SageMaker, deployed as serverless AWS architectures - Full-stack development — React, Next.js, Node.js, TypeScript, PostgreSQL, MongoDB - Cloud & DevOps — AWS Lambda, API Gateway, Terraform, GitHub Actions, Docker RECENT WORK - Built a distributed multi-agent system on AWS — five Lambda functions (one per agent), S3-backed vector storage, SageMaker embeddings, API Gateway orchestration, with a Next.js frontend on CloudFront + S3. - Built DocInsight, a 3-agent RAG pipeline (Relevance Checker → Research Agent → Verification Agent) that hybridizes BM25 and vector search and blocks unverified answers from ever reaching the user. - Built Sidekick, an autonomous task agent (browser, code, files, search) with a structurally independent evaluator and a typed feedback loop for self-correction on retry. - Added LangSmith observability across multi-agent pipelines — distributed tracing, automated eval suites, and CI regression gates in GitHub Actions. ENTERPRISE-SCALE FULL-STACK WORK - Built the scheduling and DST timezone engine for a workforce platform used by 11,000+ users across 170+ franchises in 4 countries — zero production shift drift across 50+ components. - Led a 5-engineer team reviving an abandoned multi-tenant social platform with no documentation, owning architecture decisions and code review. - Shipped multi-currency Stripe billing and reverse-engineered a legacy authorization system for a platform serving 1M+ end users. Tell me what you're trying to build — an AI agent, a RAG system, a full-stack app, or all three — and I'll tell you straight whether it's a good fit before we start.

AI engineer. 30 jobs done on Upwork, $10K+ earned, 100% job success rate. What I mostly work on: → Agent systems. LangGraph, custom orchestration, MCP. Used for things like document analysis pipelines and internal company tools. → RAG. Pinecone or pgvector setups. Tuned for production, not just plug-and-play LangChain. → Full AI products. I've shipped paid marketplace apps and multi-agent backends. Real shipped products, not demos. Some recent work I'm proud of: Column Mate. A paid Monday marketplace app I built. Live on the Monday App Store (App ID 10601212). Pricing tiers from $0 to $300 a month. It has 4 features the leading competitor doesn't have, including the only AI Parse Updates feature on the whole marketplace. Auto Blog Automation. An n8n pipeline I built for a client. Cut their article production from about 4 hours per article to under 5 minutes. Publishes to 5 platforms automatically. Business Control Center. I migrated a flat-file JSON system to Postgres for a client. 9 migrations shipped. Dashboard reads under 100ms p95. My main stack: Python, Node.js, FastAPI, PostgreSQL, n8n, OpenAI, Claude, LangChain, LangGraph, MCP, Pinecone, Whisper, ElevenLabs. I'm bilingual, Arabic and English. I handle the full system: auth, billing, infra, all of it. Not just the AI parts. If you want a working system instead of a research notebook, send me what you're trying to build.

I've spent 𝐓𝐰𝐨 𝐃𝐞𝐜𝐚𝐝𝐞𝐬 following one thread: tinkering with technology → 𝐁𝐮𝐢𝐥𝐝𝐢𝐧𝐠 𝐏𝐫𝐨𝐝𝐮𝐜𝐭𝐬 → delighting customers → disrupting industries → improving lives. That obsession took me from writing code at 𝐎𝐫𝐚𝐜𝐥𝐞 and 𝐌𝐢𝐜𝐫𝐨𝐬𝐨𝐟𝐭 to founding Sensely - an early pioneer of Conversational AI and avatars in healthcare - scaling it globally. Most AI projects fail because the engineer doesn't understand the business, and the executive doesn't understand the tech. I am the exception. I am a GenAI, MCPs, LLMs specialized AI Engineer who speaks the language of the C-Suite. You get a production-grade system that actually solves your business problem (not just a demo) which scales with your company growing. 𝐑𝐄𝐂𝐄𝐍𝐓 𝐖𝐎𝐑𝐊 - 𝐄𝐧𝐭𝐞𝐫𝐩𝐫𝐢𝐬𝐞 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐂𝐨𝐩𝐢𝐥𝐨𝐭 (𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 + 𝐑𝐀𝐆) - Support reps dug through 40k+ policy docs and still answered wrong. Built a cited-answer copilot that cut handle time and new-hire ramp. Next.js 15 RSC + TypeScript, Node/tRPC, Postgres + pgvector hybrid search with cross-encoder reranking, per-tenant cost metering, LangSmith evals in CI. - 𝐒𝐞𝐧𝐬𝐞𝐥𝐲-𝐕𝐢𝐫𝐭𝐮𝐚𝐥 𝐇𝐞𝐚𝐥𝐭𝐡 𝐀𝐬𝐬𝐢𝐬𝐭𝐚𝐧𝐭 - Co-founded and scaled an empathy-driven conversational AI avatar used by the NHS, AXA, Generali & Mayo across 35+ countries; raised $26.5M; acquired by Mediktor. - 𝐌𝐮𝐥𝐭𝐢-𝐀𝐠𝐞𝐧𝐭 𝐑𝐞𝐯𝐞𝐧𝐮𝐞 𝐎𝐩𝐬 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 (𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤 + 𝐆𝐞𝐧𝐀𝐈) - A GTM org was leaking pipeline to manual CRM hygiene and slow follow-up. Built agents that research accounts, draft outreach, and update CRM records under human approval. LangGraph over custom MCP servers, Temporal durable execution, Stripe metered billing, Next.js console for run replay and audit. - 𝐑𝐞𝐚𝐥-𝐓𝐢𝐦𝐞 𝐕𝐨𝐢𝐜𝐞 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 𝐨𝐧 𝐀𝐖𝐒 + 𝐂𝐥𝐚𝐮𝐝𝐞 - Frontline staff spent more time documenting conversations than having them. Shipped live transcription and summarization that returns a structured note seconds after the call ends. Kinesis + Transcribe streaming into Claude on Bedrock; Lambda, ECS Fargate, S3/KMS, HIPAA-eligible, CDK IaC. - 𝐂𝐥𝐚𝐢𝐦𝐬 & 𝐔𝐧𝐝𝐞𝐫𝐰𝐫𝐢𝐭𝐢𝐧𝐠 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐨𝐧 𝐨𝐧 𝐀𝐳𝐮𝐫𝐞 + 𝐎𝐩𝐞𝐧𝐀𝐈 - Reviewers hand-keyed data from PDFs and email attachments into a multi-day backlog of inconsistent decisions. Automated intake and triage so staff only touch exceptions. - 𝐌𝐮𝐥𝐭𝐢-𝐓𝐞𝐧𝐚𝐧𝐭 𝐒𝐚𝐚𝐒 𝐏𝐥𝐚𝐭𝐟𝐨𝐫𝐦 (𝐅𝐮𝐥𝐥-𝐒𝐭𝐚𝐜𝐤) - A funded startup ran pilots out of spreadsheets and needed a product customers could buy without a salesperson. 𝐖𝐇𝐀𝐓 𝐈 𝐁𝐔𝐈𝐋𝐃 I build the whole thing. The interface your users touch, the API and data model underneath it, the retrieval and agent layer that makes it intelligent, the cloud infrastructure it runs on, and the evals and dashboards that tell you it's still working next quarter. That means agentic systems and MCP integrations, RAG over messy real-world data, conversational and voice interfaces, document and workflow automation, internal copilots, and the multi-tenant SaaS products they live inside. Healthcare and insurance are where I have the deepest scar tissue, but the engineering is domain-agnostic - I've shipped for fintech, e-commerce, telecom, logistics, legal, and B2B software, and the pattern is always the same: find the workflow that costs the most, and make it disappear. 𝐖𝐇𝐘 𝐂𝐋𝐈𝐄𝐍𝐓𝐒 𝐇𝐈𝐑𝐄 𝐌𝐄 Because I optimize for your outcome, not my hours. I've been the founder staring at runway and the CPTO defending a roadmap, so I'll tell you when a feature isn't worth building, when a cheaper model gets you 95% of the result, and when the honest answer is that AI isn't the right tool for this problem. Clients keep coming back because I treat their business as the product and the software as the means. 𝐓𝐄𝐂𝐇 𝐒𝐓𝐀𝐂𝐊 - 𝐅𝐫𝐨𝐧𝐭𝐞𝐧𝐝: React, Next.js (App Router, RSC), TypeScript, Angular, Tailwind, shadcn/ui, TanStack Query, Vercel AI SDK, WebSockets/SSE, React Native - 𝐁𝐚𝐜𝐤𝐞𝐧𝐝: Node.js, NestJS, Express, tRPC, Python, FastAPI, Go, REST, GraphQL, gRPC, WebRTC, microservices, Temporal, BullMQ - 𝐆𝐞𝐧𝐀𝐈 & 𝐀𝐠𝐞𝐧𝐭𝐬: OpenAI, Claude, Gemini, Azure OpenAI, Bedrock, MCP, LangChain, LangGraph, RAG, agentic workflows, tool calling, fine-tuning, LLM evals, guardrails, voice AI (Whisper, ElevenLabs, LiveKit) - 𝐃𝐚𝐭𝐚 & 𝐕𝐞𝐜𝐭𝐨𝐫: PostgreSQL, pgvector, Pinecone, Qdrant, Redis, MongoDB, DynamoDB, Cosmos DB, Supabase, Prisma, Snowflake, Databricks - 𝐂𝐥𝐨𝐮𝐝 & 𝐃𝐞𝐯𝐎𝐩𝐬: AWS, Azure, GCP, Docker, Kubernetes, Terraform, GitHub Actions, LangSmith - 𝐈𝐧𝐭𝐞𝐠𝐫𝐚𝐭𝐢𝐨𝐧𝐬 & 𝐂𝐨𝐦𝐩𝐥𝐢𝐚𝐧𝐜𝐞: Stripe, Twilio, Salesforce, Epic/Oracle Health, HL7 v2, FHIR R4, OAuth 2.0/OIDC, SSO/SAML, HIPAA, SOC 2, GDPR If you're navigating GenAI - as a founder, executive, or enterprise team - let's talk.

Specialist in ontology engineering, semantic web, knowledge graphs, and GenAI. OWL, RDF, SPARQL, Java, python, PostgresQL, MySQL, and graphical databases like MogoDB, ArangoDB and Neo4J. SCRUM (SFC) certified. LLM, GraphRAG. LangGraph, LangChain

🏆 𝐓𝐨𝐩 𝐑𝐚𝐭𝐞𝐝 𝐏𝐥𝐮𝐬 ✅𝟏𝟎𝟎% 𝐉𝐨𝐛 𝐒𝐮𝐜𝐜𝐞𝐬𝐬 🎯+𝟓𝟎 𝐀𝐈 𝐩𝐫𝐨𝐣𝐞𝐜𝐭𝐬 𝐝𝐞𝐥𝐢𝐯𝐞𝐫𝐞𝐝 +5 years AI experience, specialized in 𝐀𝐠𝐞𝐧𝐭𝐢𝐜 𝐀𝐈 𝐬𝐲𝐬𝐭𝐞𝐦𝐬 where AI answer, queries databases, manages CRM records, runs API calls, automates workflows end-to-end. 🚀 𝐖𝐡𝐚𝐭 𝐈 𝐁𝐮𝐢𝐥𝐝 🧠 AI SaaS MVPs — Production Next.js + Claude + Stripe apps shipped in 4 weeks 🤖 Multi-Agent Systems — LangGraph + Claude pipelines, $65K+ proven in production 🏢 Enterprise RAG — Context-aware engines across Notion, Slack, SQL & Drive (10K+ docs) 📞 AI Phone Agents — Retell + Vapi + ElevenLabs for sales, support, appointment booking 🔌 MCP Servers — Custom Anthropic-spec servers exposing your data to Claude Code, Cursor, Desktop ⚙️ n8n + Claude Workflows — End-to-end automations with CRM, email, voice, web scraping 🛒 Multi-Channel Ops — Telegram, Shopify + Square + Wholesale unified in Google Sheets / QuickBooks 🛠️ 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐈𝐧 ● Claude Ecosystem — Claude Code, Cowork, MCP, Skills, Plugins, Agent SDK, OpenClawn (self-hosted) ● Voice AI Agents — Retell, Vapi, Bland, Synthflow, ElevenLabs, Deepgram, Whisper ● AI Systems — RAG, GraphRAG, SQL Agents, Multi-Agent Systems, Computer Use, Browser Automation ● Frameworks — LangChain, LangGraph, CrewAI, Pydantic AI, OpenAI SDK, Claude Code, Deep Agents 💻 𝐓𝐞𝐜𝐡𝐧𝐢𝐜𝐚𝐥 𝐒𝐭𝐚𝐜𝐤 ● AI & LLMs — Anthropic Claude · OpenAI · Gemini · DeepSeek · Llama / Ollama (local) ● Orchestration — LangChain · LangGraph · CrewAI · Pydantic AI · MCP ● Vector & Data — Qdrant · Pinecone · ChromaDB · Supabase · PostgreSQL · pgvector ● Backend — Python · FastAPI · Node.js · TypeScript · Docker · PostgreSQL ● Frontend — Next.js 14 · React · Tailwind · Vercel AI SDK · shadcn/ui ● Automation — n8n · Claude Code · Webhooks · REST APIs · Openclaw 📈 𝐏𝐫𝐨𝐨𝐟 𝐨𝐟 𝐖𝐨𝐫𝐤 • PagBank (Brazil's largest fintech) — Voice + Chat Generative AI in production • ChatADV — AI for 100,000+ lawyers, thousands of documents • $65K+ SQL Agent platform — 2+ years in production, no data incidents • Langflow (acquired by Datastax & IBM): AI Engineer & Mentor • Multi-Channel CPG Ops (hot sauce brand) — Shopify + Square + Wholesale 𝐇𝐨𝐰 𝐈 𝐰𝐨𝐫𝐤 1. 30-min scoping call — I return an architecture sketch + ballpark within 24h 2. Prototype in days/weeks with weekly demos in a shared workroom 3. Production hardening — safety rails, observability, audit logging, documentation 4. Handoff or optional maintenance retainer ⚡ Shoot me a message and let's scope your AI project — first reply within 2h.

I help organizations turn complex AI ideas into reliable, production-ready systems. I have more than nine years of experience across machine learning, NLP, computer vision, backend engineering, and full-stack product development. In recent years, I have focused on building enterprise GenAI platforms for large organizations in industries such as banking and telecommunications. My work spans agentic AI, retrieval systems, semantic data platforms, knowledge graphs, natural-language analytics, and end-to-end AI product development. I have also led large, cross-functional engineering teams responsible for taking these systems from early architecture and experimentation through deployment, evaluation, scaling, and production support. Areas I specialize in • Designing multi-agent and tool-using AI systems with LangGraph, LangChain, MCP, A2A, FastAPI, and custom orchestration layers • Building RAG solutions that connect LLMs with documents, databases, internal knowledge repositories, APIs, and enterprise platforms • Creating semantic layers, taxonomies, ontologies, and domain models for analytics and AI applications • Developing text-to-SQL and natural-language data exploration systems for Postgres, BigQuery, Teradata, and other enterprise data platforms • Implementing graph-based retrieval systems using Neo4j, vector search, metadata enrichment, entity extraction, and relationship modeling • Building complete AI SaaS applications using Python, Node.js, React, Next.js, Docker, Kubernetes, AWS, and GCP • Improving model reliability through fine-tuning, prompt optimization, evaluation frameworks, retrieval testing, guardrails, and hallucination mitigation Highlights from my experience • Led large engineering teams in building asynchronous, extensible agent platforms for enterprise troubleshooting • Reduced complex troubleshooting and resolution workflows from days to minutes • Designed and productionized hundreds of reusable tools across multiple MCP servers • Led the development of natural-language query platforms for large enterprise databases • Improved NLQ accuracy through semantic modeling, metadata enrichment, knowledge graph traversal, and domain-specific context • Built ontology-driven Knowledge Graph RAG platforms for organizing and retrieving knowledge from unstructured enterprise data • Co-founded and scaled an AI-powered video translation and dubbing product from launch to meaningful commercial revenue • Developed computer vision systems deployed across industrial locations in India and Europe Core technology stack Python, Node.js, FastAPI, LangGraph, LangChain, LlamaIndex, OpenAI, Hugging Face, PyTorch, PEFT/LoRA, React, Next.js, Postgres, BigQuery, Teradata, Neo4j, Pinecone, PySpark, Airflow, Docker, Kubernetes, ArgoCD, AWS, and GCP. I work best with clients looking to build serious AI products rather than short-lived prototypes. I can support the full lifecycle—from clarifying the problem and defining the architecture to implementation, enterprise integration, deployment, evaluation, observability, and production hardening.