At Google, I own prompt design standards for enterprise LLM application teams and build Python tooling around modern LLM APIs. I create reusable agent architectures that support developer workflows across software delivery programs.
I define retrieval-augmented generation patterns using embeddings and vector databases behind stable application interfaces. I also design evaluation pipelines for non-deterministic systems, pairing regression tests with safety checks for release reviews and policy assessments.
I use structured outputs and multi-agent architectures to resolve inconsistent results from ambiguous requests, giving developers clearer failure signals and documented technical direction. My work brings responsible AI judgment, tool-use boundaries, and compliance considerations into production enterprise applications.
Earlier, I maintained backend services at Intel and built connected-product software services at Pebble Tech. I improved API contracts, observability, containerized deployment workflows, API versioning, REST APIs, and technical documentation while mentoring engineers through architecture decisions.
