Kubernetes Engineer
In this role, you will design, implement, and optimise GPU-accelerated container platforms at scale, enabling high-performance workloads (AI/ML, HPC, LLM training) across hybrid or on-prem environments.
You will have deep expertise with both NVIDIA and Kubernetes ecosystems, including GPU scheduling, device plugins and custom operators.
Key responsibilities of the role include:
· Architecting and operating Kubernetes clusters optimised for GPU workloads, leveraging NVIDIA GPU Operator, Network Operator and DCGM
· Developing, deploying and maintaining custom Kubernetes operators and controllers to automate infrastructure services
· Integrating NVIDIA device plugins, Multi-Instance GPU (MIG) and GPU sharing features into the scheduling layer
· Optimising GPU utilisation and job placement through scheduler extensions, such as kube-scheduler plugins, Slurm and Volcano
· Collaborating with HPC, ML and DevOps teams to ensure multi-tenant, high-throughput cluster performance
· Driving observability and telemetry integrations using Prometheus, Grafana, DCGM Exporter and OpenTelemetry
· Implementing secure multi-user and multi-namespace GPU isolation, with RBAC and policy enforcement, such as OPA or Gatekeeper
· Maintaining CI/CD pipelines for Kubernetes infrastructure using GitOps, ArgoCD and FluxCD
· Contributing to infrastructure-as-code, using Terraform, Helm, and Kustomize
· Participating in performance tuning, incident response and production readiness reviews
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