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 (OpenAI and Azure OpenAI, Anthropic, Google, and open-weight models served locally through Ollama), prompt engineering for production use, structured output validated against Pydantic schemas, 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. The orchestration layer I build on is LangChain and LangGraph: in production that is a compiled state graph with conditional routing, where a query is language-detected, decomposed, rewritten, retrieved against, reranked, and resolved as separate nodes rather than one opaque prompt; in my own agent work it extends to checkpointed state across turns, middleware for conversation summarization and task planning, and a human-in-the-loop interrupt-and-resume path — built and tested, ready for the first tool that should require a person's approval before it acts; that research is what led me into the corporate agent platform I am building now, a shared runtime early in its development where any team's agents are stored configurations rather than separate codebases. I treat evaluation and operability as product requirements, concretely rather than aspirationally: LangSmith tracing wired into the production retrieval service so latency, token usage, and cost are observed instead of guessed; a blind, scored quality comparison against professional human output before the system went wide; a held-out evaluation harness that reports precision, recall, and F1 against ground truth; and retry ladders, embedding-provider fallback, and visible graceful degradation, so a bad generation is a logged and recoverable event rather than a silent one. 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.