Muhammad Waqar K.

Most AI projects don't fail because the model is wrong. They fail because nobody wired it to production properly — no evals, no retries, no observability, no plan for what happens when the LLM hallucinates on a Tuesday. That's the gap I close. I'm a senior engineer with 8+ years shipping software, the last 3 focused entirely on LLM systems: RAG pipelines, autonomous agents, and multi-step workflows that hold up under real users, real data, and real edge cases. Recent work: → Autonomous financial risk assessment agent — cut analyst review time by ~60% → Medical document analysis agent processing thousands of clinical documents weekly → Demand forecasting layer on a commerce analytics SaaS → RAG systems over messy PDF corpora (contracts, medical records, engineering specs) Stack: Python, TypeScript, LangChain, LlamaIndex, OpenAI, Anthropic Claude, Gemini, Pinecone / Weaviate / pgvector, FastAPI, AWS, Docker, n8n. How I work: — 4-week delivery blocks with weekly demos, not surprise reveals at the end — Real production code your engineers can take over, not notebook glue — Evals from day one, so you catch regressions before your users do — 3–4 hours daily overlap with US and EU business hours Who I work best with: founders and product teams treating AI as core to their offering — not a hackathon experiment — who want a partner that can own delivery end-to-end. If that's you, hit Message. First call is free, and I'll tell you honestly whether your project is a fit.