I'm an AI engineer with three years of experience taking applied AI from research to production. I own the full lifecycle, including data preparation, fine-tuning, evaluation, orchestration, and deployment, and I ship systems that hold up to real reliability, latency, and cost targets, not just demos.
My expertise spans both sides of the stack. On the model side: fine-tuning, eval harnesses, distillation, and alignment. On the systems side: multi-tenant infrastructure, semantic retrieval at scale, observability, and deterministic orchestration. I work in agentic frameworks, advanced context engineering, RAG architectures, and multi-agent workflows, and I write production-quality Python with the discipline that comes from shipping rather than prototyping.
I focus on the parts of applied AI most prototypes skip: tenant isolation, retrieval quality, conflict resolution, auditability, and regulated PII handling under GDPR. I treat reliability, latency, and cost as first-class concerns, and I have the judgment to keep generative LLMs off the hot path when deterministic code does the job better. I'm comfortable acting as a technical advisor across teams, turning ambiguous problems into shipped systems.
I want to build products real users adopt and operators trust: the durable infrastructure and clean abstractions that let teams move fast without breaking what matters.
