Senior Cloud Infrastructure, DevOps Solutions Architect

🔥 1 minute 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 full-solution validation on partner software stacks, including cluster-wide stability testing, real training-workload acceptance, and multi-day, multi-rack burn-in • Minimise time from cluster handover to first production workload by coordinating hardware bring-up, managed-service intake, and partner operations teams • Own Day 2 production stability at fleet scale, including monitoring, logging, workload orchestration, fault detection and remediation, preventive maintenance, and firmware and field-notice rollout campaigns • Assess customer environments and operate heterogeneous open platforms including Kubernetes, KubeVirt, Slurm, and GPU-aware schedulers • Integrate enterprise-grade networking and storage and enable third-party ISV workloads • Provide consultative guidance and hands-on troubleshooting across bare metal, operating systems, software stacks, container platforms, networking, and storage • Support R&D, proofs of concept, and proofs of value validating new features, architectures, and upgrade approaches • Act as technical leader for assigned accounts • Run structured knowledge transfer and enablement • Produce runbooks, onboarding materials, and best-practice guides for partner teams

🎯 Requirements

• BS/MS/PhD in Computer Science, Electrical/Computer Engineering, Physics, Mathematics, or related fields, or equivalent experience • 8+ years in managing scalable cloud environments and automation engineering roles • Proven understanding of networking fundamentals and data centre architectures • Hands-on experience managing HPC/AI clusters and NVIDIA GPU-accelerated infrastructure, including deployment, driver and CUDA toolkit management, optimisation, workload profiling, and troubleshooting • Extensive Kubernetes experience for container orchestration, resource scheduling, and scaling in GPU-accelerated and HPC environments • Experience with scheduler internals, batch schedulers such as Slurm, and mixed bare-metal/virtualised multi-tenant estates such as KubeVirt • Deep knowledge of Linux, including RedHat and Ubuntu, OS-level security, and protocols • Experience with storage solutions such as Lustre, GPFS, ZFS, XFS, and Kubernetes storage technologies • Proficiency in Python and Bash scripting • Experience with configuration management and Infrastructure-as-Code tools such as Ansible and Terraform • Experience with GitOps-based cluster lifecycle and upgrade management for large fleets • Experience with observability stacks such as Grafana, Loki, and Prometheus • Ability to measure and improve MTBI and job goodput on large GPU clusters • Experience with fault detection, drain and remediation workflows, SLO/error-budget definition, and post-incident review • Strong consultative background leading architectural reviews and presenting to executive stakeholders • Knowledge of CI/CD pipelines and container-based microservices architectures • Experience with NVIDIA GPU and Network Operators and NVIDIA Base Command Manager • Familiarity with DCGM, XID diagnostics, node-level health agents, and fleet-wide reliability intelligence • Expertise in AI-native scheduling and inference frameworks on Kubernetes, such as KAI, Grove, Dynamo, and NVIDIA Cloud Functions • Background with RDMA-based fabrics such as InfiniBand or RoCE • Exposure to Cumulus Linux, SONiC, Spectrum-X fabrics, DPU/DOCA infrastructure services, and NVLink/NVSwitch partition operations is a strong plus

🏖️ Benefits

• NVIDIA products and work on next-generation AI/HPC systems • Exposure to large-scale infrastructure projects and advanced GPU/HPC technologies • Customer, partner, and cross-functional collaboration • Technical leadership, knowledge transfer, and enablement opportunities