Most software becomes more complex with time. There are proven ways to build systems that scale with it.
A product-minded senior engineer who turns growing business complexity into software that scales — configurable platforms, real-time systems, and applied AI.
For the last 9 years I've helped teams see the patterns, generalize the solution, and turn one-off features into systems that stay flexible as they grow.
Look for the pattern. When a team keeps building the same kind of screen or workflow for slightly different cases, I turn the repeated part into something reusable — a component, a config, a shared platform.
Move behavior into configuration. I've worked on products where every new workflow meant another frontend change. Pushing the differences into configuration made most of those releases unnecessary.
Use AI where it earns its place. Computer vision, evaluation, and human-in-the-loop review — one tool in the kit, not the headline.
Component Platform
A shared UI platform turns every future product into a head start.
#MultiBrandMulti-Brand Platform
The skill in a white-label platform is drawing the line between configuration and custom code — and defending it.
#ConfigurationConfigurable Systems
Every successful workflow eventually wants to become a platform.
#AppliedCVComputer Vision
Improving a model usually starts with improving the process around it.
#MeasurementMeasurement & Analytics
An average is the story data tells about itself; a percentile is closer to the truth. Measurement is making the honest number the easy one to see.
AI Product Strategist of the Year
Recognition for hands-on engineering work behind AI-powered products — computer vision, model evaluation, and human-in-the-loop systems — and for turning applied AI from prototype into something that actually ships.
View recognition →Notes on product systems, the reality of applied AI, and how state shapes software.
A Keystroke Shouldn't Wake the Whole Form
In a configuration-driven form with hundreds of inputs, typing one character re-rendered nearly every field. The fix had little to do with memoization and a lot to do with deciding who is allowed to listen.
When the Measurement Is the Bug
Evaluating a candidate system against one already in production, the hard part was never the thing being measured. It was three numbers that looked healthy and described a different experiment than the one intended.
Human-in-the-Loop Isn't Going Away
When an AI system is accurate enough for the demo but you still can't remove the person, the missing piece isn't the model — it's who carries the cost of being wrong, and whether the system routes by that cost.
Let's build better product systems.
Whether it's frontend architecture, workflow platforms, or AI-powered products — I'm always interested in challenging problems. Have something in mind? Reach out.