My early career focused on medical image-guided diagnosis and intervention, building systems that combine medical image analysis, 3D visualization, and real-time tracking to help surgeons navigate complex procedures. That work grew into nearly two decades as a Principal Investigator leading multidisciplinary R&D teams, and it taught me how to bridge the gap between research ambition and production reality.

Over the last few years I've transitioned to cloud-native and hybrid architectures, deploying and operating AI models across cloud, on-prem, and edge environments with end-to-end MLOps pipelines. I've taken products through FDA 510(k) clearance and built the infrastructure to keep them running. Most recently, I've been hands-on building agentic applications using LangChain, LangGraph, RAG architectures, and vision-language models across heterogeneous platforms.

What shipping software and AI solutions in regulated, safety-critical environments teaches you is that the hard part is never the model or the algorithm. It's the infrastructure, the evaluation rigor, the regulatory compliance, the testing and validation, the governance, the observability, and the ability to work across engineering, clinical, and business teams simultaneously. Most AI initiatives stall because they treat these as afterthoughts.

That's exactly the problem I'm now focused on at enterprise scale. I work with large organizations to move AI out of the experiment phase and into their actual operating model. Designing agent architectures with retrieval, orchestration, routing, and observability built in from day one. Building abstraction layers across AI providers so teams aren't locked into a single vendor. Rethinking workflows from first principles around what AI makes possible, rather than wrapping AI around processes that were designed for a pre-AI world.