Artificial Intelligence

My artificial-intelligence skill is organized around modern large language models and the engineering discipline that turns them into production systems. I work fluently across the current LLM surface — multiple model families and providers, prompt engineering for production use, structured output, streaming and tool-calling patterns, token and context-window budgeting, and the quality and cost trade-offs that decide whether an LLM idea ever reaches real users.

On top of that base I practice retrieval-augmented generation, agentic orchestration, and evaluation as first-class disciplines. In retrieval that means chunking, embeddings, vector search, full-text search, hybrid ranking with reciprocal rank fusion, cross-encoder reranking, query decomposition and rewrites, two-layer storage for semantic matching against parent documents, and multilingual retrieval patterns. In agents it means multi-agent orchestration with explicit specialization and handoff, context engineering and short-term / long-term memory design, tool-calling through the Model Context Protocol (MCP) for grounded access to knowledge bases and systems of record, and the discipline of validating agent output against verifiable sources. I treat evaluation as a product requirement — blind quality comparisons, source-grounded correctness checks, latency and cost envelopes, and operational observability — because LLM systems that are not measured drift silently.

I apply this skill with a platform-first posture — build reusable primitives (retrieval, agent tooling, memory, access models) once and compound multiple products, teams, and workflows on top — and I reinvest what I learn back into the open-source MCP and agent-tooling ecosystem on GitHub, so the internal work stays aligned with how the field is actually moving. The concrete systems this skill has shipped into — a shared retrieval backbone, an AI localization system, an agent-facing MCP server for a system-of-record platform, multi-agent research workflows, and earlier RAG and ML work — are listed in the Related Projects, Related Experience, and Related Certifications panels above.