Senior Infrastructure Engineer

Key Responsibilities

• Own the remote management, monitoring and troubleshooting of our datacenter servers, primarily NVIDIA GPU servers (DGX/HGX-class or similar).
• Manage relationships with datacenter, hardware and OEM vendors, including procurement, support escalation and lifecycle management.
• Support and maintain high-performance computing (HPC) infrastructure, including high-speed interconnects (InfiniBand) at the fabric level.
• Support the deployment, configuration and day-to-day operation of orchestration platforms — Kubernetes and Slurm — for GPU workloads.
• Define and establish standards and processes for hardware provisioning, server lifecycle management, and datacenter operations.
• Diagnose and resolve hardware, firmware and low-level infrastructure issues, coordinating with vendors where necessary.
• Work closely with the Head of Sovereign AI Infra and the broader infrastructure team to ensure hardware, orchestration and platform layers work together reliably.
• Contribute to capacity planning and infrastructure scaling decisions as the platform grows.

Essential Experience

• 8+ years of experience in an infrastructure, systems or datacenter engineering role.
• Hands-on experience with remote management of datacenter servers, including hardware troubleshooting, firmware/BIOS management and vendor coordination.
• Experience managing hardware/OEM vendor relationships.
• Fluency in high-performance computing (HPC) environments and high-speed interconnects (InfiniBand) — hands-on experience is ideal, but strong working fluency is acceptable.
• Experience with orchestration platforms for compute clusters, particularly Kubernetes and Slurm.
• Demonstrated ability to define and introduce standards and processes from scratch, ideally in a startup or early-stage environment.
• Comfortable working in a small, fast-moving team with significant ownership.

Highly Regarded

• Direct hands-on experience with NVIDIA GPU server platforms (DGX, HGX or similar).
• Experience supporting AI/ML training or inferencing workloads at the infrastructure level.
• Familiarity with Linux and the open-source infrastructure ecosystem.