About
I'm an engineer and builder working on applied AI systems that need to hold up in real workflows. I'm a founding engineer at Architect Health, where I build AI and data infrastructure for healthcare. I also spend time on agent systems, developer tools, and the operating details that make AI useful inside teams.
I like the part of building where product judgment, systems design, and operations meet the reality of how people actually work. The question I keep coming back to is simple: what has to be true for a person or team to trust AI with meaningful work?
Before this I was at LinkedIn, and I've spent years applying machine learning to healthcare workflows, decision support, and data systems used by providers. That work shaped how I think about AI: reliability, privacy, and accountability are part of the product, especially when the workflow affects care. Originally from Houston, now based in Austin.
How I Build
- Stay close to the work. I want to understand the workflow at the level where the product either earns trust or fails. Strategy gets sharper when it is tied to the actual system.
- Reliability is part of care. AI products work best when people can trust the output, recover from mistakes, and understand who owns the decision. That takes structure, not just a better prompt.
- Be careful with core dependencies. When a product depends on AI, infrastructure choices affect privacy, cost, reliability, and the customer experience. I prefer designs that keep those tradeoffs visible.
- Design the operating model. The hard part is rarely one model call. It is permissions, context, review, audit trails, escalation, and the human workflow around the AI.
- Keep a person accountable. Agents can help with real work, but a product still needs a responsible owner. Oversight matters most when the work affects people.
Outside of Work
- Animal rescue and adoption advocate
- Strength training and outdoor running enthusiast
- Sports fan. Rockets, Astros, Texans, Texas Longhorns.
- Currently playing Whiteout Survival