I'm a software engineer focused on cloud infrastructure and security. Currently I'm building SAP AI's infrastructure.

My background spans platform security at SAP and high-throughput microservices. I'm happiest close to the infrastructure: Kubernetes, GitOps, and event-driven architectures.

I previously built a telemetry system for an automotive client that monitors and draws analytics for millions of vehicles across 5 continents.

• Be candid and stand up for your ideas.
• Don't take professional feedback personally.
• Give and receive feedback often while utilizing the 4As of feedback.
• Don't join a meeting that has no agenda.
• Don't sit in a meeting where you have nothing to contribute or to learn.
• Leave it better than you found it.

• Contributing to SAP's Crossplane Providers.

• Treat AI output as a first draft — always review, test, and validate before using.
• Be specific and provide context in your prompts; vague inputs produce vague outputs.
• Keep a human in the loop for decisions that carry risk or affect people.
• Understand the limitations of the model you're using — don't assume it's always right.
• Don't feed sensitive or proprietary data into tools you don't control.
• Evaluate AI-generated code the same way you'd evaluate a colleague's pull request.
• Use AI to augment your thinking, not replace it.
• Leverage MCP (Model Context Protocol) servers to connect AI directly to your tools and data sources — less copy-pasting, faster access to the right information.

Books and articles I've read and can recommend. If you have suggestions, please reach out — always happy to pick up a gem I haven't noticed yet!

• The Software Engineer's Guidebook by Gergely Orosz.
• Clean Code by Robert C. Martin.
• 21 Lessons for the 21st Century by Yuval Noah Harari.
• No Rules Rules by Reed Hastings & Erin Meyer.
• Surrounded by Idiots by Thomas Erikson.
• Lift Off by Eric Berger.
• Shoe Dog by Phil Knight.
• Algorithms to Live By by Brian Christian & Tom Griffiths.
• The Culture Map by Erin Meyer.