AI Infrastructure Technical Systems Engineer

What you'll do

• Design and maintain large-scale bare-metal GPU clusters for NVIDIA and AMD hardware.
• Architect multi-tenant compute and control planes using Kubernetes, Red Hat OpenShift, and HPC cluster managers.
• Optimize high-throughput, low-latency backend fabrics including RDMA and RoCEv2.
• Develop IaC and GitOps deployment pipelines using Ansible, Terraform, Python, and Go.
• Automate firmware baselines, BIOS/BMC configurations, kernel driver updates, and certificate rotations.
• Optimize distributed workload scheduling and GPU quota governance using Slurm, Kueue, Volcano, and Ray.
• Establish end-to-end observability standards using OpenTelemetry, Prometheus, and Splunk.
• Lead root-cause analyses for high-impact incidents and mentor junior engineering talent.

What we're looking for

• Bachelor’s degree in Computer Science, Engineering, IT, or a related discipline.
• Progressive experience in enterprise systems engineering, HPC, AI infrastructure, or SRE.
• Proficiency in Python, Go, or Bash with extensive experience in Ansible, Terraform, CI/CD, and GitOps.
• Advanced expertise in production Kubernetes, Red Hat OpenShift, and HPC workload managers like Slurm, Kueue, Volcano, or Ray.
• Strong hands-on understanding of AI backend network fabrics, RDMA technologies (RoCEv2), and high-speed multi-hundred-gigabit topologies.
• In-depth knowledge of enterprise Linux, kernel modules, memory subsystems, GPU drivers, and out-of-band management (Redfish, IPMI).
• Master’s degree in a technical discipline or an additional 2+ years of equivalent specialized engineering experience (preferred).
• 3+ years of experience managing enterprise server platforms at scale, including automated hardware provisioning (preferred).