I build production AI systems end to end — for operations where correctness, real-time performance, and trust actually matter.
I taught myself to code in 2020 — no CS degree, no bootcamp, just a laptop and stubbornness. Seven months in, I landed my first engineering job at Kero Sports, where I spent the next two years building the backend systems behind their micro-betting products for basketball, football, and baseball. Real-time pricing, automated trading, and market infrastructure that runs in production for 150+ sportsbooks including Caesars. That's where I learned what "production" really means — hard real-time, no second chances.
These days I work independently, shipping AI-native products end to end — designing the data model, building the backend and infrastructure, and carrying it all the way to a product people actually use. I'm drawn to deep, unglamorous domains where the architecture decisions genuinely matter: healthcare and revenue cycle, real-time systems, and agentic workflows where AI and humans run on the same rails. A current slate of that work is below.
Coming up self-taught shapes how I build. I'm pragmatic, ship-first, and skeptical of complexity that exists to impress other engineers. The code that ships is the code that matters.
I'd rather get something real in front of users this week than design the perfect system over six. The system gets better when the feedback loop is real.
Event-driven boundaries, governed entry points, audited state changes. Boring tech in the foundation buys you the freedom to be ambitious at the edges.
Same auth, same audit, same policy engine. If an agent can do it, a human reviewer can see it. No shadow lanes.
Came up self-taught — never had time for over-engineered patterns that only impress in code review. The code that ships is the code that matters.
If you've got a project, an AI problem you're stuck on, or just want to talk — I'd rather have a real conversation than trade emails forever.