About

I build AI systems that are meant to be used, not just demoed.

My work sits at the intersection of agentic AI, retrieval systems, LLM platforms, and production engineering. I like designing systems where models, tools, and data work together to solve real problems — whether that means multi-agent orchestration, vector retrieval, OCR pipelines, or enterprise AI deployment.

Over the course of my career, I’ve moved through neuroscience research, healthcare analytics, government research, data science, and enterprise AI engineering. That path gave me a strong bias toward systems that are grounded, testable, and useful in the real world.

Today, I focus on building enterprise AI systems that include:

• multi-agent orchestration
• retrieval-augmented generation
• embeddings and vector databases
• tool-calling architectures
• evaluation and guardrails
• production-ready LLM workflows

I enjoy the engineering side of AI just as much as the strategy side: choosing the right model for the job, designing the surrounding system, and getting the full thing to work reliably.

Education

• Johns Hopkins University — Master’s Degree
• James Cook University, Australia
• The College of St. Scholastica

Interests

Outside of work, I’m into mountain biking, flying my paramotor, scuba diving, and building side projects that usually start as “just for fun” and turn into something much bigger.