Profile Summary

Infrastructure Engineer for AI/data platforms, with strong expertise in Kubernetes-based runtime platforms, cloud infrastructure, security engineering, and reliable operations. Focus on enabling scalable, secure deployment of AI, data, and application services in regulated environments.

Core Responsibilities / Focus

• Design and operate cloud-native infrastructure for AI/data workloads (including GPU-enabled workloads)
• Build and manage Kubernetes platforms (node pools, autoscaling, GPU scheduling, workload isolation)
• Implement secure platform foundations: identity, secrets, networking, access control, and service-to-service auth
• Develop and maintain IaC, CI/CD, and GitOps delivery workflows
• Support app/model gateways, service mesh, and event-driven serving layers (Istio, Knative)
• Provision and operate shared platform services (databases, message brokers, etc.)
• Establish SRE practices, especially observability, reliability, and incident readiness
• Enable compliant infrastructure patterns suitable for security- and audit-sensitive domains

Core Skills (Must-Have)

• Go (primary)
• TypeScript
• Docker / Docker Compose
• Kubernetes (incl. node pools, GPUs, autoscaling)
• Azure (ideally with exposure to other cloud providers)
• Security Engineering (OAuth, Managed Identities, secrets management)
• Networking
• Git
• GitOps
• Build pipelines / CI/CD
• Infrastructure as Code (IaC)
• App / Model Gateways
• Istio
• Knative
• Service provisioning (databases, message brokers, etc.)
• Site Reliability Engineering (SRE), especially observability

Preferred / Nice-to-Have

• Python
• Java
• Documentation practices

Domain Advantage

• Experience operating platforms in regulated environments with strong requirements for:
• security
• traceability
• access governance
• operational resilience