I am an AI engineer focused on building reliable, production-grade agentic systems. I independently shipped a Microsoft 365 AI agent from an empty repository to a live, transactable Microsoft Commercial Marketplace listing, owning product design, engineering, compliance, certification, and billing integration.
I build LLM applications around practical agent orchestration: plan/act/observe loops, function calling, tool retrieval, RAG, semantic memory, model routing, evaluation, and failure recovery. My work emphasizes making autonomous systems useful, measurable, secure, and controllable in real production environments.
I created a multi-agent workflow engine that converts plain-English requests into validated control-flow graphs of reusable specialist agents. The platform uses human approval gates before actions execute, static graph validation, shared agent libraries, and learning from corrected examples.
My technical background spans Python, TypeScript, SQL, cloud platforms, data engineering, machine learning, APIs, and distributed systems. I have experience with Azure, AWS, GCP, PostgreSQL, BigQuery, streaming pipelines, vector search, and deployment automation.
Before transitioning into software and AI, I taught undergraduate mathematics for eight years and worked in semiconductor physical design at Intel. These experiences strengthened my ability to reason rigorously, communicate complex concepts clearly, and solve difficult systems problems.
I am especially interested in senior AI engineering opportunities involving LLM agents, workflow automation, retrieval systems, evaluation infrastructure, and dependable AI products.
