Building production systems

I've worked across the entire stack at companies like FreeWheel and Charter — designing database schemas, building API architectures, deploying to Linux servers, and maintaining the kind of systems that process millions of ad impressions daily. Understanding how every layer connects is what separates code that works from systems that scale.

Now that work extends into AI systems and mobile. I build the same way — clean architecture, well-defined boundaries, thorough documentation — whether I'm standing up a Flask web application, wiring up a fleet of MCP servers that give LLMs real-time access to live data, or shipping a native SwiftUI iPhone app against a versioned API contract. Maintainability matters especially in AI, where the pace of change is relentless.

I've automated enterprise reporting with Python and pandas, and built diagnostic tooling that became a go-to resource across engineering teams. Today that means a chat platform that streams from either a local GPU or a cloud API behind one interface, with a planning pass that decides which of my MCP servers each request actually needs — and a file pipeline that classifies uploads by magic bytes before an LLM ever sees them. The common thread is building tools that people actually rely on.