My path into AI wasn't a straight line, and I like it that way. It started in marketing and data science, using machine learning to shape business decisions before "data-driven" was a buzzword. That curiosity about what the data was actually telling us pulled me deeper : into ML engineering, where I built the models and pipelines myself, and eventually into platform engineering, where I got obsessed with a different question: how do you make AI work reliably, at scale, when real people depend on it?

That question is basically my job now. As an Manager of AI/ML Infrastructure at MasterControl, I help build agentic AI systems for regulated industries like healthcare and life sciences, spaces where "it mostly works" isn't good enough, by owning the AI/ML infrastructure that lets those systems run reliably in production. When your product touches quality management for medical devices or pharma, trust isn't a feature, it's the whole point. I think about AI infrastructure less like plumbing and more like the thing that decides whether innovation actually reaches people or just looks good in a demo.

Somewhere along the way I also co-founded two companies, filed a patent, and picked up a habit of mentoring engineers trying to break into AI, because someone did the same for me once. These days I split my time between building production systems and sharing what I've learned along the way, especially around why so much of the industry's advice on AI infrastructure is borrowed from a world that no longer applies.