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Hi! I’m Hamza, an AI Engineer with 4+ years of industry experience building, deploying, and optimizing real-world AI systems. I specialize in Agentic AI, AI Automation, LLMs, RAG, AI Agents, document intelligence, and production ML systems. I help businesses turn manual workflows into intelligent, automated systems that can understand information, make decisions, use tools, and take actions. 🤖 Agentic AI & AI Automation ✅ AI Agents & multi-agent systems ✅ Agent orchestration with LangGraph, LangChain & LlamaIndex ✅ Tool/function calling and API integrations ✅ AI workflow automation and intelligent task execution ✅ MCP (Model Context Protocol) integrations ✅ Human-in-the-loop workflows & escalation ✅ AI agents connected to databases, CRMs, APIs and external tools ✅ Custom AI copilots and business assistants 🧠 LLMs & Generative AI ✅ OpenAI GPT models, Mistral, LLaMA and Hugging Face models ✅ Prompt engineering & structured outputs ✅ LLM orchestration and model integration ✅ Fine-tuning & inference workflows ✅ NLP, text classification and information extraction ✅ Document intelligence and unstructured-to-structured data processing 📚 RAG & Knowledge-Based AI ✅ Production RAG pipelines ✅ Semantic search & hybrid retrieval ✅ FAISS, Pinecone, PGVector and vector databases ✅ Hugging Face / Sentence Transformers embeddings ✅ Advanced chunking and retrieval strategies ✅ Reranking and retrieval optimization ✅ Document Q&A and knowledge-base assistants ✅ RAG evaluation and hallucination reduction ⚙️ AI Backend & APIs ✅ Python ✅ FastAPI & REST APIs ✅ PostgreSQL & SQL ✅ LangChain / LangGraph ✅ Docker & containerized AI applications ✅ Streamlit dashboards and AI interfaces ☁️ MLOps & Cloud ✅ AWS & GCP cloud deployment ✅ Docker & CI/CD ✅ MLflow & Airflow ✅ Scalable ML/LLM pipelines ✅ Model deployment, monitoring and optimization ✅ Cloud-based AI automation 📄 Document AI I have real-world experience building systems that process unstructured documents and transform them into structured, usable information using LLMs and NLP. For example, I developed an insurance claims processing pipeline using LLMs to convert unstructured claim information into structured data, which was then used by downstream ML models. I have also worked on OCR-based document verification and document intelligence applications. 🚀 What I Can Build for You • AI Customer Support Agents • Internal Knowledge Assistants • Multi-Agent AI Systems • RAG Chatbots • AI Document Processing Pipelines • AI-powered Business Automation • AI Sales & Lead Qualification Agents • Database & API-connected AI Agents • AI Copilots • LLM-powered Workflow Automation • Intelligent Document Extraction • Custom ML/AI APIs • Production-ready RAG and Agentic AI platforms My goal isn't simply to connect an LLM to an application. I build AI systems that can retrieve knowledge, reason over information, use tools, interact with external systems, and automate real business workflows. If you’re looking to build an AI agent, RAG system, intelligent automation workflow, LLM application, or production ML solution, feel free to message me. Let’s turn your workflow into an intelligent AI system.

Hello, I am a software engineer with 22+ years of work experience on C, C++, Python, Javascript, Chromium(Android, iOS, Linux, Windows and MacOS), Embedded systems, Node, React, Linux. Previous clients include Samsung, Cisco, Comcast/Sky, Arris, OceanHero, Seekr, CrankWheel, BrightSign, Garrison. I am a problem solver - I gauge the design approach to be followed. depending on the current tech stack. I work on Embedded Systems as well as Desktop Windows, Linux and Mac OSX.

🥇 Top Rated | 100% Job Success 🥇 20+ Years in Tech | AI/ML/NLP | Automation 🥇 OpenClaw / Hermes Claw Expert for production-ready AI agents, MCP tools, RAG bots, n8n workflows, and business automation I’m Asif, an Open Claw / Hermes Claw Expert, and AI Automation Engineer. I build production-ready OpenClaw AI agents that connect to real systems, take real actions, automate workflows, update CRMs, process documents, scrape data, generate reports, and trigger business operations. Recent Open Claw builds include CorpusIQ for MCP-based business-tool orchestration, GTM100 for AI GTM/email automation, social media automation across X/Twitter, LinkedIn, TikTok, Instagram, Facebook, and n8n automation for AI cold calling, Gmail replies, CRM updates, Google Sheets, Monday crm, Google Docs, VPS deployment, and reporting workflows. I can help you build: ✅ OpenClaw & HermesClaw AI Agents AI agents that call APIs, query databases, process PDFs/docs/sheets, update HubSpot/Salesforce/Airtable/Notion, and run multi-step workflows. ✅ MCP Servers & Model Context Protocol Tools Custom MCP tools for CRMs, databases, APIs, internal apps, permissions, secrets, audit logs, and safe execution. ✅ RAG Bots & Knowledge Assistants Chat over PDFs, SOPs, wikis, tickets, manuals, websites, and internal docs using Pinecone, Qdrant, Chroma, Weaviate, LangChain, LlamaIndex, OpenAI, and Claude. ✅ n8n AI Automation n8n workflows for lead routing, email automation, AI cold calling, Google Sheets, Monday crm, Airtable, Gmail, Google Docs, CRM sync, webhooks, and APIs. ✅ Social Media Automation OpenClaw agents for content posting, comment monitoring, DM reply drafting, public profile scraping, lead enrichment, CRM updates, and human-in-the-loop approvals. ✅ VPS Deployment & Production Setup Docker, SSL, VPS management, logs, retries, monitoring, guardrails, documentation, and clean handoff. Keywords: Open Claw Expert, OpenClaw Developer, Hermes Claw expert, Open Claw AI Agent, Open Claw Automation, AI Automation, AI Agent Developer, MCP Server Developer, Model Context Protocol, RAG Developer, n8n Expert, Python Automation, API Integration, Webhooks, VPS Deployment, Social Media Automation.

I architect scalable AI systems that move from "experimental demo" to "clinical/enterprise deployment." With 5+ years in technical architecture and a background as a Founder in healthcare AI, I bridge the gap between technical complexity and business ROI. Key Metrics & Impact Production Deployment: Shipped 8+ live applications across the UK, USA, and India with local server execution and high-security deployment. Healthcare Reliability: Selected for NHS Propel HealthTech Accelerator (2025) to develop diagnostic support tools focusing on data security. Cost & Efficiency: Built Python-based simulation models for behavioral analysis and ROI forecasting, reducing manual analysis time. Core Expertise Agentic Frameworks: Autonomous workflows using LangGraph, CrewAI, and custom Python state machines. Voice Intelligence: Enterprise-grade agents (Twilio, ElevenLabs) for automated clinical scheduling. Production RAG: High-performance retrieval using Weaviate, Pinecone, and advanced chunking. LLM Orchestration: Integration of Gemini, GPT-4, and Claude 3.5 into Python/React stacks. Technical Stack Python (FastAPI, Flask), Node.js, Docker, GCP (Certified), Firebase, OpenAI API, Google Vertex AI, LangChain. Strategy MBA in Finance & Marketing. I prioritize code quality, SOLID principles, and long-term maintainability over quick-fix prototypes. Suggested Skills Tags Python, LangChain, RAG, System Architecture, GCP, AI Engineering, Healthcare Technology, Voice AI, Docker, FastAPI.

Agents don't fail because the model is bad. They fail because the tools they're handed are unnavigable, the retrieval is unmeasured, and nothing catches a regression before the client does. I build the layer underneath: production MCP servers (Model Context Protocol), RAG pipelines with measured accuracy, and the eval harnesses that keep both honest. 🏆 Top Rated Plus · $350K+ earned · 8,000+ hours billed ━━━━━━━━━━━━━━━━━━━━━━ RECENT PRODUCTION RESULTS 🔌 Built and shipped a production MCP server (FastMCP, Python) that gives a client's analysts direct agent access to their own domain data — questions that used to need an engineer now get answered in the chat window. It runs in production behind an agent service on AWS Fargate over stdio, and in Claude Desktop and Claude Code. I designed the tool surface for progressive discovery — broad list, then filter and count, then drill down — so models navigate 15K+ records without blowing their context, and built credential-gated tool registration with a read-only-by-design data layer so pointing an agent at live data is safe. 850+ tests, and an eval harness I run across model versions before shipping changes, so a model upgrade can't silently break agent behaviour. 🤝 Wrote the agent that drives it, too — a project-level Claude Code subagent with a curated tool allowlist, in-prompt gates derived from real production failures, and a cost-tier routing matrix that picks the cheapest transport likely to work. Building the tool surface and the agent that consumes it is a different skill from wiring up one API. 🤖 Built a RAG extraction service (FastAPI + Celery + Pinecone, two-tier model routing with a per-model cost estimator) turning messy documents into structured data across ~11K projects from 11 registry sources. Measured on a golden dataset, accuracy went from 33% to 91% F1 on one extraction task and 43% to 84% on another — the difference between a pipeline nobody trusted and one the team runs unattended. Every output traces back to its source document. 🧠 Built a second production RAG system on a medical knowledge graph (FastAPI, Neo4j, MongoDB) with character-level span citations, so a disputed claim takes seconds to check rather than an afternoon. Application-layer tenant isolation with dedicated tests proving no cross-tenant leakage. 2,700+ tests; I wrote roughly two-thirds of the codebase. 🌍 Built and operate a scraping platform covering 20 sources behind enterprise anti-bot protection — Cloudflare-class WAFs and Incapsula, a rotating datacenter proxy pool plus a residential tier for the hardest targets, and fallback transport chains that step up only when they have to. 225K+ documents collected to date. 50+ scheduled pipelines, around 30 of them daily, with per-source error recovery and alerting. ━━━━━━━━━━━━━━━━━━━━━━ WHAT I DO ✔️ AI integration & agent infrastructure — MCP (Model Context Protocol) servers with FastMCP, tool surfaces designed for how models actually search, Claude Desktop and Claude Code integrations, custom Claude Code subagents, prompt engineering, eval harnesses ✔️ LLM & RAG backends — Anthropic/OpenAI/Gemini APIs, Pinecone, vector search with RRF fusion, structured extraction from messy documents, measured accuracy against golden datasets, cost routing that sends the easy 80% to cheap models ✔️ Web scraping & data extraction — Playwright, Selenium, ZenRows; resilient access to protected sources, proxy management, scheduled fleets via Celery, PDF/Excel/Word extraction, normalization into clean schemas ✔️ API development — FastAPI, Flask, Django; auth, rate limiting, background jobs, clean documentation ✔️ Distributed systems — Celery, RabbitMQ, Redis; retries, idempotency, fault tolerance under real load. Redis caching at 85-95% hit rate, typically 10-50x faster responses ✔️ Production ops — Docker, AWS, PostgreSQL/MongoDB/Neo4j, 110+ zero-downtime migrations on a single project, CI-gated test suites running 2,500-4,900 tests on my largest systems ━━━━━━━━━━━━━━━━━━━━━━ HOW I WORK ✅ I own systems end-to-end: architecture → implementation → deployment → monitoring → handover docs. ✅ I'll tell you when an LLM is the wrong tool — and what to use instead. Cheaper for both of us than finding out in week three. ✅ Failures surface where you'll see them: Prometheus/AlertManager into Slack with per-alert templates, Grafana and Loki for dashboards and logs, scheduled digests and a daily data-feed tripwire. I get paged, not you. ✅ Most of my $350K+ comes from repeat clients and multi-year engagements. ━━━━━━━━━━━━━━━━━━━━━━ 📍 UK-based (GMT/BST) If you need agent tooling that real models can navigate, an LLM pipeline whose accuracy you can actually check, or scraping infrastructure that survives contact with real anti-bot systems — send me a couple of lines about your project and I'll tell you straight away whether I'm the right fit.

OVERVIEW: I build AI systems that extract structured intelligence from business documents — not chatbot wrappers. My background is unusual: CFA charterholder, former arbitrage trading desk developer, now dev team lead at a major investment bank. I've spent years building production infrastructure across finance, real estate, and enterprise software. What I build now: • Document intelligence pipelines — upload PDFs, ask questions in plain English, get structured answers with source citations and confidence scores • RAG systems — extraction, chunking, vector search, LLM synthesis, evaluation (RAGAS) • Multi-provider LLM integration — Anthropic, OpenAI, Groq, Ollama — with automatic failover and cost optimisation • Full-stack Python platforms — FastAPI backends, production deployment, cloud infrastructure Recent work: • Built a production RAG pipeline for financial document analysis (lease agreements, facility agreements, fund documents) — PDF extraction, schema discovery, hybrid search (BM25 + vectors), LLM synthesis with source citations • Multi-provider LLM abstraction layer with rate limiting, key rotation, and graceful degradation • Enterprise-grade deployment infrastructure — blue-green deployments, agent orchestration, health monitoring Stack: Python, FastAPI, PostgreSQL, Redis, OpenSearch, Docker, DigitalOcean/AWS. LLM providers: Claude, GPT, Groq, Ollama. I work fast, I ship production code, and I don't over-engineer. If you need an AI system that actually works in a business context — not a demo — let's talk.