About the role
Requirements:
- 5+ years of experience building distributed systems or infrastructure platforms with deep Kubernetes expertise.
- Strong programming skills in Go and/or Python.
- Familiarity with Kubernetes controller development frameworks such as Kubebuilder, Operator SDK, or controller-runtime.
- Deep understanding of Kubernetes internals, including the API server, etcd, scheduler, controller manager, and kubelet.
- Hands-on experience designing Kubernetes networking, including Gateway API, CNI plugins, service load balancing, and hybrid cloud architectures.
- Experience designing and operating multi-NIC Kubernetes clusters using NVIDIA Network Operator, SR-IOV device plugins, or equivalent tooling.
- Strong understanding of RDMA networking protocols and NCCL configuration for distributed GPU workloads.
- Experience with Kubernetes GPU scheduler frameworks and GPU pool management, including MIG partitioning and preemption policies.
- Hands-on experience deploying and operating KubeRay for distributed Ray workloads.
- Experience with GPU asset lifecycle management, bare-metal provisioning automation, and GitOps-based CD tooling such as Argo CD.
- Familiarity with GPU technologies including NVIDIA CUDA, NVLink, NVSwitch, device plugins, and the GPU Operator ecosystem.
- Experience with observability tooling such as Prometheus, Grafana, and OpenTelemetry.
- Strong debugging and performance optimization skills across GPU driver stacks, RDMA networking, and distributed Kubernetes infrastructure.