A path that wasn't straight — but always pointed at the same thing.

I tested industrial automation machines — CNC lathes, switchgears — adjusting drives mechanically and working close to the hardware. But it was the programming side, writing in C and C++ and working with logic gates, that pulled me toward quality: I cared less about building a thing and more about whether it actually worked, reliably, every time.

Three years testing Sharp's document-solutions products across three platforms — a cloud web portal, Windows desktop, and mobile. This is where I built my foundation: structured manual testing on real consumer products, and hands-on automation with the full WebDriver family. I learned what disciplined testing actually looks like inside a mature enterprise framework — writing tests that hold up, reproducing defects clearly, and working alongside senior engineers to close them.

I joined as the team moved toward building software in-house, and built the QA function from scratch: standards, processes, and a reusable automation framework integrated with CI. But the real shift was in how I thought about testing. Working closely with designers and developers — translating designs into requirements, shaping how a product felt before a test was written — taught me that quality isn't about catching bugs. It's about understanding what a product means to the person using it, and noticing where reality drifts from that.

A Master's with a machine-learning emphasis, where I learned to build data pipelines and run inference across different types of structured and unstructured datasets. It also gave me a grounding in data ethics — understanding how data is sourced, used, and governed, which matters more than ever in an industry building on top of it. And it shaped how I work day to day: I use AI deliberately in my development practice, orchestrating the design and direction myself and keeping what I build transparent rather than a black box I can't explain.