Muhammad Shoaib A.

I build production AI systems that actually work not demos. RAG pipelines, LangGraph agents, LLM integrations, and full-stack backends. Live examples: → TheGoodScholar: multi-agent RAG system, LangGraph + Pinecone + GPT-4, deployed on AWS. Grounds every response in 28k+ authenticated Islamic texts with citations. Real users, real queries. → HyperionLabs: technical lead on AI financial platform. RAG agent processing 250k+ crypto transactions, confidence scoring, human escalation paths, audit trails. 95% time savings, 90%+ accuracy in production. → Goldman Sachs: document automation pipeline using AWS Textract + GPT-3.5. 80% reduction in manual data entry, 95%+ accuracy on financial documents. → BBC Bitesize: React components and Node.js services for one of the UK's largest digital platforms. What I specialise in: AI & LLM Engineering: OpenAI, Anthropic Claude, LangChain, LangGraph, RAG systems, vector databases (Pinecone, pgvector, Chroma), prompt engineering, multi-agent orchestration, confidence scoring, human-in-the-loop workflows. Backend Development: Python (FastAPI, Django REST), Node.js (NestJS, Express), PostgreSQL, Redis, WebSockets, microservices, REST APIs. Cloud & DevOps: AWS (ECS/Fargate, Lambda, S3, Textract, RDS), GCP (Cloud Run, Vertex AI), Docker, Kubernetes, GitHub Actions, LangSmith, Datadog. Frontend: React, Next.js, TypeScript, Tailwind CSS, Redux, responsive design. I work best with clients who have a real problem to solve and want someone who takes ownership from architecture decisions through to deployment and monitoring. I'll push back on bad approaches early rather than build something that doesn't hold up. 9 years production experience. Goldman Sachs, BBC, funded startups. Clean code. Clear communication. No hand-holding needed.