About me

I'm an AI Engineer with a software-engineering foundation. After several years building full-stack applications in Java, Spring Boot, Node.js, and React, I moved into applied AI — building retrieval-augmented systems, tool-using agents, and fine-tuned models. I'm completing the IBM AI Engineering and Applied AI Engineering programs and hold an MBA in IT Management. I care about AI systems that are tested, grounded, and honestly evaluated.

How I work

Grounded outputs over confident guesses. Defined tool contracts with documented failure modes. Honest evaluation — real metrics, confusion matrices, and frank analysis of where a model falls short. Clean service boundaries, so the AI layer and the application around it can each evolve without breaking the other.

Background

Fifteen-plus years across software development and IT infrastructure: Spring and AWS microservices deployed with Docker, Kubernetes, and Jenkins; enterprise full-stack applications in Java, C#, React, and Angular; a stint teaching computer science. Most recently, the three core patterns of modern LLM systems — RAG, agents, and fine-tuned classifiers — through CodePath's AI201 program, alongside a home lab where I self-host AI services on my own infrastructure.