I help teams find the useful product hiding inside an ambiguous problem.

For a decade, I’ve built software across product surfaces, frontend platforms, APIs, data systems, and internal tools. The through-line is not a particular framework. It is making good decisions while the problem is still taking shape—and staying close enough to the code to turn those decisions into something people can use.

At SINAI, that increasingly meant project-lead work: aligning product, design, engineering, and business context; writing RFCs; sequencing delivery; and building the highest-leverage pieces myself. I’ve led multi-contributor efforts in AI-assisted data ingestion, reusable workflows, notifications, modeling systems, and frontend quality.

I use prototypes as decision tools. A good prototype can expose the real product question, make a sales conversation concrete, or reveal a technical constraint before a team commits to the wrong abstraction. When the idea proves out, I prefer foundations that can grow into production over throwaway theater.

AI is part of my daily engineering practice, with a deliberately human review loop. I use it to investigate unfamiliar systems, pressure-test plans, draft implementation paths, and parallelize narrow research. I still own the judgment: what the product should do, what evidence is trustworthy, what belongs in the architecture, and what ships.

I care about calm collaboration, clear systems, and software that earns its complexity. If that sounds useful for what you’re building, send me a note.