I'm an AI infrastructure engineer focused on Kubernetes, GPU systems, and developer tooling for reliable AI workloads. My work spans Azure Kubernetes Service (AKS), distributed training and inference infrastructure, scheduling systems, runtime performance, and agent-assisted engineering workflows.

I specialize in turning complex research and platform ideas into practical systems that can be tested, operated, and improved in real environments. I bring a systems-level perspective across cloud infrastructure, ML runtimes, and developer experience, with an emphasis on reliability, observability, and repeatable engineering practices.

• GPU orchestration and scheduling for AI workloads
• Kubernetes-native infrastructure for distributed training and inference
• Runtime performance, reliability, and production readiness
• Developer tools and agent workflows that make engineering teams more effective

• Current role: Member of the Azure Kubernetes Service (AKS) team at Microsoft
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