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Senior Cloud Infrastructure and Reliability Solutions Architect

NVIDIA· Architect, Solutions
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Hybrid (Courbevoie, France), Hybrid (Germany), Hybrid (Spain)full-timePosted 1w ago

About the role

Day 2 Site Reliability Engineer for the NVIDIA Cloud Partner (NCP) Day 1 / Day 2 Operations team in the EMEA region. Owns production stability, automation, fault detection/remediation, and proactive maintenance across multi-thousand-GPU fleets in partner estates, with a tool-agnostic approach across open-source and in-house stacks.

Responsibilities

  • Own Day 2 production stability and reliability at fleet scale across EMEA NCP partners: define, measure and improve MTBI and job goodput on multi-thousand-GPU clusters, and hold the error budget for the platforms you support.
  • Build and operate the fault detection to remediation loop—GPU, node, fabric and storage fault signatures, automated triage, node drain, cordon, reset and return-to-service workflows—so failures are caught and cleared before they cost training jobs.
  • Run preventive maintenance and proactive firmware and field-notice rollout campaigns across partner fleets (e.g. CPLD and switch firmware levels), including risk assessment, staged rollout, maintenance-window planning and verification at scale.
  • Own observability and reliability tooling for GPU fleets: Prometheus/Grafana/Loki, DCGM and XID diagnostics, node-level health agents such as GPUd and NVSentinel, and fleet-wide reliability intelligence—extending or integrating them into the partner’s own home-grown stack.
  • Lead incident response and post-incident review in collaboration with NVEX, TAM and partner operations teams: drive root-cause analysis on escalations, track corrective actions to closure, and convert every recurring incident into automation, a health check or a runbook.
  • Own Kubernetes node lifecycle and reliability engineering on heterogeneous open stacks—upstream Kubernetes, KubeVirt, Slurm and GPU-aware schedulers—covering node health policy, upgrade and reboot orchestration, and safe workload draining on GPU-accelerated nodes.

Qualifications

  • BS/MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related fields, or equivalent experience.
  • 8+ years in SRE, production engineering or large-scale infrastructure operations roles.
  • Reliability Engineering Practice: Demonstrated ownership of production reliability at scale—SLO and error-budget definition, on-call and incident command, blameless post-incident review, and measurable improvement of availability or mean time between interruptions.
  • GPU & HPC Fleet Operations: Hands-on experience operating HPC/AI clusters and NVIDIA GPU-accelerated infrastructure—driver and CUDA toolkit lifecycle, firmware management, workload profiling, and troubleshooting across CPUs, GPUs and high-speed interconnects.
  • Kubernetes at Scale: Extensive expertise in running Kubernetes in GPU-accelerated and HPC environments—node lifecycle, upgrades and draining, scheduler behaviour, batch schedulers such as Slurm, and mixed bare-metal/virtualised (e.g. KubeVirt) multi-tenant estates.
  • Observability & Automation: Proficiency in Python and Bash, Infrastructure-as-Code and configuration management (e.g. Ansible, Terraform), GitOps-based fleet lifecycle and upgrade management, and observability stacks (Prometheus, Grafana, Loki) for monitoring, alerting and building fault-tolerant systems.
  • Linux, Storage & Networking Fundamentals: Deep knowledge of Linux (RedHat, Ubuntu), OS-level tuning and security, data centre networking fundamentals, and parallel/enterprise storage (Lustre, GPFS, ZFS, XFS) as they affect cluster stability.

Skills

Required:SRE principles (SLOs, error budgets)GPU/HPC fleet operationsKubernetes at scale (GPU/HPC)Observability and automation

Benefits

  • Not specified in the provided description.
Last updated Oct 23, 2026. Interested? Apply on the company site.

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