Data Center Operations Engineer — Key Requirements

Location: Dallas–Fort Worth, USA (on-site)

 

Role Overview

Hands-on role responsible for operating and maintaining high-performance computing (HPC) and AI infrastructure, with a particular focus on GPU-based systems. The priority is keeping GPU hardware running reliably, resolving failures quickly, and deploying new equipment.

 

Key Responsibilities

  • Install, maintain, and troubleshoot NVIDIA GPU servers and HPC infrastructure.
  • Diagnose hardware failures and respond to monitoring alerts to minimize downtime.
  • Perform firmware updates, component replacements, and hardware repairs.
  • Support high-performance networking and storage connected to GPU compute systems.
  • Manage hardware assets, spare parts, vendor support, and RMAs.
  • Support new hardware deployments and data center expansions.
  • Investigate incidents, identify root causes, and improve monitoring and automation.
  • Participate in a 24/7 on-call rotation.
  • Travel across North America to support new data center deployments.

 

Required Experience

  • 3+ years of experience in data center operations, hardware engineering, or HPC infrastructure.
  • Hands-on experience with NVIDIA GPU servers or equivalent high-performance computing systems.
  • Strong server hardware troubleshooting skills, including firmware management and component replacement.
  • Experience with high-performance networking and storage.
  • Understanding of HPC and AI workloads.
  • Experience maintaining hardware availability in a production environment.

Preferred Experience

  • Experience with NVIDIA HGX systems.
  • Supermicro or Dell HGX hardware certifications or training.
  • Experience with greenfield data center deployments.
  • Knowledge of cooling, power distribution, and data center infrastructure.
  • Networking or hardware certifications; ITIL knowledge.

Ideal Candidate

A technically strong, hands-on hardware engineer with genuine GPU infrastructure experience who can troubleshoot complex failures, maintain uptime, and support the deployment of large-scale AI infrastructure.