AI Engineering & LLM Development

Both, and they are different things. I integrate LLM-powered features into products — retrieval-augmented answers, classification, extraction, chat, copilots — and I also engineer my own delivery with AI rigor. What I sell you is the former: production AI features that hold up. The latter is just why I can build them faster without cutting corners.

Retrieval-augmented generation grounds an LLM's answers in your own data — your docs, records, or knowledge base — instead of letting it guess. You need it when answers must be correct and traceable to a source. I build the full path: embeddings, vector search, retrieval, and the guardrails that keep answers grounded.

Evals and guardrails. Before anything ships, I build an evaluation harness that scores the model on real examples, add guardrails that constrain what it can output or do, and wire in observability so failures are visible, not silent. An AI feature that fails quietly is worse than no feature.

Frontier hosted models (Claude, OpenAI) and open-source or self-hosted models, chosen per task on cost, quality, latency, and privacy. For sensitive data I run self-hosted models in Docker so nothing leaves your environment. I am not married to one vendor.

Discipline and verification. I run multiple models that review each other's work, hold every one of them to a centralized, version-controlled engineering standard, and verify output against real criteria before it ships. The AI accelerates the work; it does not get to decide what "done" means.

Yes. Most of my AI work lands inside existing products — a new endpoint, a background job, a copilot in the UI — built to the same money-path and reliability standards as the rest of the app, with cost controls so a runaway prompt loop never becomes a runaway bill.

Yes. I build multi-step agent workflows where a model plans, calls tools, and acts across several steps, with explicit limits on what it is allowed to do. The engineering challenge is control and reliability over many steps, which is exactly where most agent demos fall apart and where I focus.

No. I design for data privacy: self-hosted open models in Docker when the data is sensitive, and providers and configurations that exclude your data from training when using hosted models. Your data stays yours.