I have 15+ years of experience building and scaling startups in regulated industries such as finance, law, and human subjects research. I specialize in accelerating ML and AI development within the scientific Python ecosystem.
My recent roles have focused on architectural and strategic work at organization scale. Representative projects include establishing Terraform patterns at a large financial services provider, building a SOC 2 compliance program for an LLM legaltech startup, and designing a Bazel build migration at a robotics and AI neolab.
These projects have spanned multiple quarters and involved tool selection, architectural decisionmaking, drafting cross-team roadmaps, and even hiring key team members. I have scaled out my own contributions by building relationships across teams, setting standards and policies, and educating peers on industry best practices. Recently, I’ve been focused on responsible adoption of AI coding agents.
Working at scale is efficient, but I also have hands-on experience at seed stage startups where success depends on performing as a “team of one”. When executing independently, I am a fast and effective individual contributor with over twenty years of experience writing code (primarily in Python).
Outside of my day job, I aim to contribute to the tech community through continuing education. I’ve been a conference organizer for DevOpsDays, a conference speaker, and even an occasional podcast guest. I’ve also worked with O’Reilly to teach an infrastructure-as-code course and author a book chapter in Seeking SRE.
Areas of technical experience include:
- Python
- Kubernetes
- AI Agents
- GPU Compute
- Bazel
- Apache Airflow
- Amazon Web Services
- Google Cloud Platform
- Terraform
- Site Reliability Engineering
