AI Platform Engineer

• Design and build Kubernetes Custom Resource Definitions (CRDs) and operators for ML workloads, GPU node pools, and RayService CRDs.
• Architect and operate Kubernetes networking layers including Gateway API, Service Load Balancers, and API gateways for hybrid cloud connectivity.
• Design, deploy, and operate multi-NIC Kubernetes clusters with RDMA-enabled networking.
• Implement and optimize RDMA networking using GPUDirect RDMA, RoCE, and InfiniBand for distributed GPU workloads.
• Build and operate a custom AI-aware GPU scheduler with topology-aware placement, preemption, and GPU defragmentation.
• Design and manage GPU pool management systems across on-premise and cloud-burst GPU environments.
• Deploy and operate KubeRay infrastructure for distributed Ray clusters supporting training and inference workloads.
• Implement cloud bursting and spot or preemptible GPU scheduling to improve utilization.
• Automate GPU asset provisioning, node configuration, and cluster lifecycle management using Infrastructure-as-Code and GitOps.
• Implement networking policies, multi-tenant isolation, RBAC, and security controls across the Kubernetes environment.
• Build observability for GPU utilization, NCCL communication, scheduler decisions, and network throughput.
• Collaborate with ML Platform, AI Research, and Networking teams to optimize infrastructure for training and online inference.
  
   

What We’re Looking For

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field.
• 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.