AI agents and LLM pipelines - RAG, evaluation and guardrails that survive production

I build the LLM layer of your product and make it reliable enough to keep in production.

WHAT YOU GET

- RAG pipelines: chunking and indexing strategy, hybrid retrieval, reranking, and an evaluation harness that scores the retriever separately from the generator
- Agents and multi-step workflows: tool calling, state, retries, budget and timeout control, human-in-the-loop checkpoints
- Guardrails: input and output validation, refusal and fallback paths, PII handling, prompt-injection resistance
- Evaluation and observability: golden sets, regression runs in CI, per-request tracing, token and latency cost tracking

WHY IT MATTERS

Most LLM features work in a demo and fall apart on real traffic. I measure retrieval quality instead of guessing, and wire the metrics into CI so a prompt or index change is a number you can see before you ship it.

BACKGROUND

10+ years as a backend engineer, most recently building a petabyte-scale ETL platform with LLM processing layers on top. Python, Go, TypeScript, vector databases, OpenTelemetry, AWS.

HOW WE START

Send me the problem and whatever exists today. You get an approach, a rough scope and an estimate before any work starts. Short audits and prototypes welcome; longer engagements billed hourly with weekly written updates.