Model Context Protocol (MCP): building standardized client-server interfaces that allow LLMs to safely read contexts, query databases, and execute verified tools.
Artificial Intelligence & Machine Learning
Building production AI systems, not conversational toys.
My AI engineering work focuses on the deterministic software surrounding probabilistic models: tool calling, Model Context Protocol (MCP), state machine orchestration, synthetic data evaluation, and automated agent workflows.
Agentic AI architecture: stateful multi-agent execution loops, memory persistence, human-in-the-loop approvals, and automatic recovery from hallucinations.
Automation and intelligent systems: transforming repetitive human processes into robust, self-healing background automation pipelines.
Evaluation & benchmarks: creating ground-truth assertions, regression test suites, and quantitative metrics before deploying AI into mission-critical paths.
Production realities: optimizing token costs, latency caching, local inference via Ollama, structured JSON output validation, and graceful deterministic fallbacks.
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