Responsibilities
- Design, deploy, and operate the control plane for an AI-operated GPU cloud
- Run production Kubernetes clusters optimized for GPU workloads at scale (100–10,000 GPUs)
- Configure Nvidia GPU operator, device plugin, MIG, and GPU time-slicing policies
- Implement topology‑aware scheduling using GPU locality, NVLink domain awareness, and network rail affinity
- Develop Custom Resource Definitions (CRDs) for GPU workload lifecycle management
- Integrate Slurm, Ray, and Kubeflow with Kubernetes
- Enforce multi‑tenant isolation through namespaces, network policies, resource quotas, RBAC, and pod security standards
- Automate BMaaS provisioning, tenant onboarding, lifecycle, and reclamation
- Build Terraform providers and modules for infrastructure‑as‑code across GPU clusters
- Define and meet SLIs/SLOs for cluster availability, job completion rates, and provisioning latency
- Automate incident management, escalation, and post‑incident reviews
- Operate monitoring with Prometheus, Grafana, Alertmanager, and PagerDuty
- Automate GPU node failure detection, drain/cordon/taint, and workload rescheduling
- Make the control plane safe for automated AIOps remediation
- Enable automated workflows for predicted GPU faults without customer impact
- Launch BMaaS for external tenants with self‑service onboarding
- Publish and meet cluster availability and job‑completion SLOs
Requirements
- 5+ years in Kubernetes operations, with at least 2 years managing GPU workloads on K8S
- Deep understanding of Nvidia GPU operator, device plugin, and GPU scheduling in K8S
- Experience with topology‑aware scheduling and GPU‑specific resource management
- Hands‑on experience building multi‑tenant K8S platforms with strong isolation guarantees
- Experience with bare‑metal server provisioning and lifecycle automation (Ironic, MAAS, or custom)
- Proficiency in Terraform, Helm, and GitOps workflows (ArgoCD/Flux)
- Strong SRE background: SLI/SLO frameworks, incident management, capacity planning
- Experience with Prometheus, Grafana, and alerting at scale
- Strong programming skills in Go or Python for operator/CRD development
- AIOps aptitude — you think of the K8S control plane as an execution surface for automated remediation, not just a scheduler
- You've either wired an autoscaler/remediator loop into K8S or you can design one
- Runbook‑as‑code mindset — every SRE playbook you write should be executable by the platform
Core Competencies
Demonstrates expertise in Kubernetes operations, particularly in managing GPU workloads and implementing topology‑aware scheduling. Proficient in automation, incident management, and building multi‑tenant platforms with strong isolation guarantees.
Highest‑signal resume keywords
- Kubernetes Operations
- Nvidia GPU Operator
- Terraform
- Prometheus
- Incident Management
ATS Optimization Keywords
Hard Skills
- GPU Workload Management
- Topology‑Aware Scheduling
- Custom Resource Definitions (CRDs)
- Multi‑Tenant Kubernetes Platforms
- Bare‑Metal Server Provisioning
- Programming in Go
- Programming in Python
- AIOps
- SLI/SLO Frameworks
- Runbook‑as‑Code
Industry Keywords
- BMaaS
- Resource Quotas
- RBAC
- Pod Security Standards
- Network Policies
- Capacity Planning
- Incident Management Automation
- Workload Rescheduling
- GPU Time‑Slicing Policies
- Network Rail Affinity
Tools & Technologies
- Kubernetes
- Slurm
- Ray
- Kubeflow
- Grafana
- Alertmanager
- PagerDuty
- Helm
- GitOps
- ArgoCD
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Senior GPU Cloud, K8S Expert Arbeitgeber: Jobtailor
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