Senior GPU Cloud Kubernetes Expert SRE SME

You will design, deploy, and operate production Kubernetes control planes for GPU workloads. You will manage GPU scheduling, tenant isolation, bare-metal provisioning, infrastructure as code, monitoring, incident response, and automated remediation. You will build workflows that detect faults, reschedule workloads, and improve reliability.

Responsibilities

• Operate production Kubernetes clusters for GPU workloads
• Manage NVIDIA GPU Operator, device plugins, MIG configuration, and GPU time-slicing
• Implement topology-aware GPU scheduling
• Develop CRDs for GPU workload lifecycle management
• Integrate Slurm, Ray, and Kubeflow with Kubernetes
• Implement multi-tenant isolation through namespaces, network policies, quotas, RBAC, and pod security standards
• Automate bare-metal provisioning, tenant onboarding, lifecycle management, and reclamation
• Develop Terraform providers and modules
• Define and manage SLIs and SLOs
• Automate runbooks, escalations, and post-incident reviews
• Operate Prometheus, Grafana, Alertmanager, and PagerDuty
• Automate GPU-node fault detection, draining, cordoning, tainting, and workload rescheduling
• Enable safe automated remediation workflows

Requirements

• At least 5 years of Kubernetes operations experience
• At least 2 years managing GPU workloads on Kubernetes
• Knowledge of NVIDIA GPU Operator, device plugins, and GPU scheduling
• Experience with topology-aware scheduling and GPU resource management
• Experience building multi-tenant Kubernetes platforms
• Experience with bare-metal provisioning and lifecycle automation
• Proficiency in Terraform, Helm, GitOps, ArgoCD, or Flux
• SRE expertise in SLI/SLO frameworks, incident management, and capacity planning
• Experience with Prometheus, Grafana, and alerting at scale
• Go or Python programming skills for operator and CRD development
• Experience designing or implementing automated remediation
• Runbook-as-code mindset