Lead Product Manager, AI Platforms & Agents — I turn frontier AI capabilities into reliable products and reusable platforms.
I build applied AI systems end to end: agent platforms, enterprise retrieval, MCP servers and gateways, and the developer tooling around them. Product ownership and production Python in the same pair of hands — problem discovery, quality criteria, architecture, implementation, deployment, adoption.
- Product ownership — I start with the problem: users, constraints, success criteria, and the adoption path — before any code.
- Platform thinking — I build reusable capabilities — retrieval, gateways, agent runtimes — rather than isolated AI demos.
- AI-native engineering — Coding agents multiply my implementation speed; architecture, review, and accountability for outcomes stay mine.
- Evaluation and verification — I define invariants, regression tests, and quality gates before trusting model or system behavior.
- Production and adoption — A system counts when it is deployed, observed, supported, and measurably used.
- MCP Context Server — persistent agent memory: full-text, semantic, and hybrid retrieval behind MCP, on PyPI and in the official MCP Registry
- Claude Code Toolbox — one-command Claude Code environments for real teams
- CXR Draft Auditor — a fine-tuned 4B medical model paired with a stock 4B parser, with a held-out evaluation harness
- The enterprise systems — a shared retrieval platform, a production YouTrack MCP server, an AI localization product with measured savings — are written up on alexfeel.info
alexfeel.info · LinkedIn · X




