Senior Cloud Infrastructure and Reliability Solutions Architect

🔥 4 minutes ago

🌐 France, United Kingdom, +2 more countries – Remote

🇫🇷 France – Remote 🇬🇧 United Kingdom – Remote 🇪🇸 Spain – Remote 🇩🇪 Germany – Remote

⏰ Full Time

🟠 Senior

💻 Solutions Engineer

👻 Ghost score 11%

What it means: how likely this posting is sitting open without active hiring.How to read it: 60% or higher means we think this is a ghost. Lower scores are less of a worry.How we score it: age, reposts, open roles vs team size, how fast jobs close, and whether pay is listed.Fine print: an estimate from public data, not a judgment of the company. Real jobs can score high, so treat it as a hint, not a fact.

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NVIDIA

WebsiteLinkedInAll Job Openings

10,000+ employees

Founded 1993

🏥 Healthcare

🏭 Manufacturing

🤖 Artificial Intelligence

Healthcare • Manufacturing • Artificial Intelligence

NVIDIA is a leading technology company specializing in accelerated computing and artificial intelligence. NVIDIA pioneers advancements in graphical processing units (GPUs), cloud computing, data centers, and virtual reality, with a focus on gaming, automotive, healthcare, and robotics industries. The company's innovations, such as NVIDIA Omniverse, transform traditional digital processes by enabling high-fidelity simulations and rendering tasks. Their applications span various industries, from autonomous vehicles using NVIDIA DRIVE to healthcare solutions with NVIDIA Clara, and AI-driven analytics and workflows.

📋 Description

• Own Day 2 production stability and reliability at fleet scale across EMEA NVIDIA Cloud Partner estates • Define, measure and improve MTBI and job goodput on multi-thousand-GPU clusters and hold platform error budgets • Build and operate automated fault detection-to-remediation workflows for GPU, node, fabric and storage failures • Automate triage, node drain, cordon, reset and return-to-service workflows • Run preventive maintenance and proactive firmware and field-notice rollout campaigns, including risk assessment, staged rollout, maintenance-window planning and verification • Own observability and reliability tooling for GPU fleets, integrating Prometheus, Grafana, Loki, DCGM, XID diagnostics, GPUd and NVSentinel into partner stacks • Lead incident response and post-incident reviews with NVEX, TAMs and partner operations teams • Drive root-cause analysis, track corrective actions to closure, and convert recurring incidents into automation, health checks or runbooks • Own Kubernetes node lifecycle and reliability engineering across upstream Kubernetes, KubeVirt, Slurm and GPU-aware schedulers • Cover node health policy, upgrade and reboot orchestration, and safe workload draining on GPU-accelerated nodes

🎯 Requirements

• 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 • Demonstrated ownership of production reliability at scale, including SLO and error-budget definition, on-call and incident command, blameless post-incident review, and measurable improvement of availability or mean time between interruptions • Hands-on experience operating HPC/AI clusters and NVIDIA GPU-accelerated infrastructure, including driver and CUDA toolkit lifecycle, firmware management, workload profiling, and troubleshooting across CPUs, GPUs and high-speed interconnects • Extensive expertise running Kubernetes in GPU-accelerated and HPC environments, including node lifecycle, upgrades and draining, scheduler behaviour, Slurm, and mixed bare-metal/virtualised multi-tenant estates • Proficiency in Python and Bash, Infrastructure-as-Code and configuration management such as Ansible and Terraform, GitOps-based fleet lifecycle and upgrade management, and Prometheus, Grafana and Loki • Deep knowledge of Linux, including RedHat and Ubuntu, OS-level tuning and security, data centre networking fundamentals, and parallel/enterprise storage including Lustre, GPFS, ZFS and XFS • Experience defining and running proactive firmware, microcode or field-notice rollout campaigns across a large production fleet • Familiarity with DCGM, XID diagnostics, GPUd, NVSentinel, node-level health agents and fleet-wide reliability intelligence • Experience with NVIDIA GPU and Network Operators and NVIDIA Base Command Manager • Practical background in AI-native scheduling and inference frameworks on Kubernetes, such as KAI, Grove, Dynamo and NVIDIA Cloud Functions • Expertise in RDMA-based fabrics such as InfiniBand or RoCE; exposure to Cumulus Linux, SONiC, Spectrum-X, DPU/DOCA and NVLink/NVSwitch partition operations is a plus