Full-stack engineer specializing in backend systems, cloud infrastructure, and QA automation.

I started my career as a software engineering intern at Jacobs Engineering Group supporting NASA projects, where I built automation tools and got my first taste of what it means to write code that actually matters. That early experience set the tone for how I approach development: build things that work reliably, automate what can be automated, and always think about the person on the other end.
Since then I've worked across the full stack — from writing QA automation suites in Python at a healthcare organization to shipping production web apps with payment integrations, real-time dashboards, and cloud infrastructure at my current role. I've managed AWS deployments, built CI/CD pipelines, consolidated messy internal tooling into clean unified systems, and migrated large datasets without breaking anything in production.
Beyond my day-to-day work, my deepest passion is applying machine learning to medical problems. I believe we're at an inflection point where AI has the genuine potential to save lives — catching what clinicians might miss, speeding up diagnoses, and making expert-level analysis accessible everywhere. I've been pursuing this hands-on, most recently building a 3D U-Net deep learning model to detect intracranial aneurysms in brain CT scans as part of the RSNA 2024 Kaggle competition. It's the kind of problem I want to keep solving — where getting it right isn't just good engineering, it actually matters to real patients.
I'm a CS grad from UCF, based in Tampa, and always looking for problems worth solving.