Engineering the Future of Network Operations.

I began my career when "network automation" was just a handful of TCL scripts and a lot of manual verification. I've spent the last two decades building the infrastructure for the world's largest networks at Amazon and Microsoft Azure. Today, I'm focused on ensuring that the next shift, AI-assisted operations, is as robust and reliable as the automation foundations we've spent years building.

The throughline has always been the same: helping network engineers use code to solve real-world problems. That started with Python and structured automation as the practical answer to clicking through GUIs at cloud scale. Now it means bridging the gap between traditional network engineering and intelligent, AI-driven networks. Along the way I picked up three US patents in IP telephony and network engineering, and contributed to a handful of Python open source projects.

The public side of my work has been one long argument that network engineers should write code, even when the tooling pushes back. First it was Python and structured automation as the practical answer to clicking through GUIs. Then it was bringing the same discipline to operations at cloud scale. Now it's making AI-assisted workflows reliable enough to actually point at production. Books, talks, classes, and the podcast are different shapes of the same project.