Senior Cloud Infrastructure and Reliability Solutions Architect

• 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

• 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