About Me

I build systems with intent, but I start with the problem before the technology.

My curiosity for engineering started in childhood — taking apart computers, reinstalling operating systems and experimenting with networks simply to understand how things worked beneath the surface. That curiosity eventually became the way I approach software: understand the users, data, constraints and dependencies first, then decide what the system should look like.

My strongest work sits where software, data and AI meet real-world workflows. I build production AI systems with deterministic logic, evaluation, observability and human review around the models rather than treating AI as a black box. Whether I’m working with computer vision, LLMs or asynchronous cloud workloads, the goal is the same: make complex technology reliable and useful to the people operating it.

As the founder behind ScoutMind, I’ve worked across the entire lifecycle of a product — researching scouting workflows, designing the architecture, building the product and AI pipeline, deploying infrastructure, evaluating system quality and iterating when reality exposes weaknesses in the original assumptions. That end-to-end ownership is the kind of engineering I enjoy most.

I care about technical depth, but I care just as much about judgment: knowing what to automate, what should remain deterministic, where AI adds real value, when an abstraction is worth creating and when the simplest implementation is the better engineering decision.

Every project is part of a longer trajectory. I want to keep working on increasingly difficult systems where product judgment, engineering depth and ownership matter at the same time.