Senior Data Center Engineer – AI/ML

Senior Data Center Engineer – AI/ML & GPU Platforms Location: Remote Experience: 7+ Years Type: Full-time Role Overview We are seeking a highly skilled Senior Data Center Compute Engineer to design, build, and operate GPU-enabled compute platforms for AI/ML and high-performance workloads. This role is heavily focused on Kubernetes, virtualization, orchestration platforms, and GPU infrastructure, with responsibility for building and managing scalable, production-grade GPU compute fabrics. The ideal candidate will have deep hands-on experience in Kubernetes cluster deployment and lifecycle management, virtualization platforms, and GPU hardware management, enabling reliable and high-performance AI workloads across on-prem and hybrid data center environments. Key Responsibilities - Design, deploy, and operate GPU-enabled compute infrastructure for AI/ML, HPC, and accelerated workloads. - Build and manage Kubernetes clusters at scale, including: - Cluster bootstrap, upgrades, and lifecycle management - High availability control planes and worker nodes - Multi-tenant and multi-cluster environments - Implement GPU scheduling, isolation, and sharing within Kubernetes (MIG, device plugins, GPU operators). - Deploy and manage virtualization platforms (VMware, KVM, OpenStack, or similar) supporting AI and container workloads. - Design and operate compute orchestration platforms spanning VMs, containers, and bare-metal nodes. - Integrate GPU servers (NVIDIA A100, H100, L40S, etc.) into Kubernetes and virtualization environments. - Automate compute and cluster provisioning using Ansible, Terraform, Helm, and scripting (Bash/Python). - Optimize compute performance, GPU utilization, and resource efficiency across clusters. - Manage bare-metal provisioning, OS imaging, and firmware lifecycle for compute nodes. - Collaborate with networking and storage teams to deliver a fully integrated AI compute fabric. - Implement monitoring, logging, and capacity planning for compute and GPU resources. - Maintain detailed documentation for cluster architecture, compute design, and operational runbooks. Required Skills & Qualifications - 7+ years of experience in data center compute or platform engineering roles. - Strong expertise in Kubernetes deployment and management, including: - Production-grade cluster design - Upgrades, scaling, and troubleshooting - Kubernetes scheduling and resource management - Hands-on experience with virtualization platforms such as VMware, KVM, OpenStack, or equivalent. - Solid understanding of container runtimes, orchestration, and cloud-native architectures. - Experience managing GPU hardware and drivers, including: - NVIDIA GPU installation and firmware - CUDA, NVIDIA drivers, and GPU operators - Proficiency in automation and IaC tools (Ansible, Terraform, Helm). - Strong Linux administration skills (RHEL, Ubuntu, CentOS). - Experience with performance tuning and capacity planning for compute-intensive workloads. - Excellent troubleshooting skills across OS, Kubernetes, virtualization, and GPU layers. Preferred / Good to Have - Experience building GPU compute fabrics / GPUaaS platforms. - Knowledge of NVIDIA technologies such as MIG, NVLink, NVSwitch, GPUDirect, and CUDA ecosystems. - Familiarity with containerized AI/ML frameworks (Kubeflow, Ray, MLFlow). - Exposure to bare-metal Kubernetes (RKE2, OpenShift, kubeadm, MAAS). - Experience with monitoring and observability tools (Prometheus, Grafana). - Understanding of hybrid cloud compute models and on-prem to cloud integrations. - Kubernetes certifications (CKA / CKAD) or virtualization certifications are a plus.