Building Scalable And Accountable AI Systems

Q1. Could you start by giving us a brief overview of your professional background, particularly focusing on your expertise in the industry?

With more than 18 years of hands-on experience, I help organizations transform experimental tech projects into production ready platforms that are secure, reliable, and cost effective. My background spans enterprise infrastructure and automation, cloud native technologies, and platform engineering.
I started by working with Linux systems, enterprise automation, and large scale infrastructure. As the tech landscape changed, I dove into containers, Kubernetes, hybrid cloud, DevOps, and more recently, AI infrastructure. I’ve worked as a Red Hat engineer and supported organizations managing complex environments in highly regulated industries and across teams worldwide.

Today, a significant part of my work involves production AI infrastructure. This includes designing Kubernetes based GPU platforms, improving GPU utilization, introducing multi-tenancy, deploying model serving systems, implementing observability, and managing generative AI operating expenses. I also advise engineering leaders on the governance and security boundaries required for AI agents and automated systems.

I am a Docker Captain, a speaker at KubeCon + CloudNativeCon Europe 2026, and the author of eight technical books covering automation, Kubernetes, and cloud technologies, including Kubernetes Recipes. In addition to consulting and …