Every system I build starts with a constraint: token cost, retrieval accuracy, or response latency, and works backward to an architecture that holds up under real users and real data. If it can't survive production, it doesn't ship.

I build multi-agent systems where each agent has a defined role, explicit tool access, and clear handoff logic. Autonomy comes from structure, not from hoping a single prompt handles everything. Coordinator patterns, not chaos.

RAG pipelines only work when the context is right. I build retrieval layers with semantic chunking, vector search, and source-aware filtering, so the model answers from your data, not its imagination. Built this for live company data at Exponentia.ai.

LLM integration means the AI layer touches the frontend, the API, and the data pipeline. I own all three, from the Angular or React interface down to the FastAPI service and the retrieval backend. One engineer, full ownership.