LLM Engineering

My LLM engineering skill is about the distance between a working prompt and a working system. A model call that behaves in a notebook still has everything ahead of it: schema-validated structured output so downstream code never parses free prose, explicit fallback when a generation comes back malformed, retry ladders and provider failover when an API degrades, token and context-window budgeting, and model tiering that matches each stage of a pipeline to the cheapest model that does the job well. I build those layers in Python, and I treat every one of them as product surface rather than plumbing — a bad generation should be a logged, recoverable, visible event, never a silent wrong answer.

In production this shows up as systems, not techniques. A retrieval platform whose every model-calling stage asks for Pydantic-validated structured output and carries its own degradation path, so one bad generation costs a stage instead of a request. A localization system that pairs strong models with terminology and translation-memory grounding, and whose output was scored blind against professional human translation before it went wide. An LLM gateway that fronts multiple providers with accounting and failover. A durable memory layer for agents, with embeddings, hybrid retrieval, and reranking behind a standard protocol. And in a one-week hackathon build, a fine-tuned 4B medical model paired with a stock 4B parser shipped as a working demo under hard constraints. Observability is wired in rather than promised: LangSmith tracing on the serving path, so latency, token spend, and cost per request are observed instead of guessed.

Measurement is where I hold this skill to account, and where I am deliberately extending it: the shipped systems carry per-system quality checks — the blind translation evaluation, the medical demo's held-out harness reporting precision, recall, and F1, the retrieval platform's per-stage score telemetry — and building that discipline into a systematic, reusable evaluation practice is the capability I am now developing in the open, starting with a benchmark for agent memory retrieval. The concrete systems are listed in the Related Projects and Related Experience panels above.