The detour is the method.

I didn't take the traditional path into engineering. I started in business development and marketing, learning how to listen, shape strategy, and connect ideas to outcomes. That foundation eventually led me into tech - and now, nearly a decade into engineering, it's clear that detour is what defines how I build.

My background spans linguistics (BS), business (MBA), data science (MS), and computer science (MS). It wasn't a master plan - but it gave me a systems view of problems: how language, data, software, and business intent intersect to create products that actually work in the real world.

As an engineering leader, my focus is straightforward: ship products that matter. I work closely with stakeholders to turn ambiguity into clear requirements, translate complex data into decisions, and guide teams toward scalable, well-architected solutions. I sit naturally at the intersection of engineering, data science, and business strategy, not because it's trendy, but because it's effective.

AI has reset the standard for engineering. One curious, resourceful engineer can now deliver what once required an entire team. Iteration loops are finally fast. Agile is no longer a theory - it's the default. Engineers are no longer just implementers; each one is an architect and a product builder.

That shift comes with new challenges. AI can generate code faster than humans can reasonably review, making traditional TDD daunting if approached naively. The answer isn't to slow down - it's to be systematic. Place tests where they matter to keep AI on track. Encode requirements and acceptance criteria directly into evaluation, validation, and automation pipelines. That's where I excel.

I'm particularly strong at automating these workflows and laying the operational foundations that let teams move fast without sacrificing correctness. Over the years I've found that when teams struggle with velocity, it's rarely a lack of resources; AI took that constraint off the table. It's an operational failure: how the team is run. That makes management matter more than ever in the age of AI. If the vision exists, engineering leaders are responsible for architecting a roadmap with a clear path forward and running the execution that delivers it.

with AI, clarity is leverage_

I'm deeply curious, always experimenting, and grounded in first-principles thinking. Above all, I believe great products come from strong teams - built on trust, ownership, and pride in what we ship. It's not about perfection; it's about momentum and impact.

Feel free to browse some of my past projects,
send me an email, or connect with me on LinkedIn.