Senior Data Center Engineer – AI/ML

19 hours ago

india, IN DC Tech Consulting Full time
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.


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