Key Responsibilities

• Lead and mentor a team of system engineers responsible for delivery, operations, escalations, and technical improvement.
• Manage and optimise OS lifecycle for GPU and CPU nodes, including patching, kernel tuning, driver and firmware updates, configuration hardening, and automation.
• Oversee bare-metal provisioning and deployment for GPU platforms, including NVIDIA stack components such as CUDA, drivers, NCCL, and container runtimes.
• Manage Kubernetes (k8s) clusters supporting GPU workload orchestration, including autoscaling, scheduling, node health, multi-tenant resource isolation, and capacity allocation.
• Run and enhance container platforms (Docker/CRI-O), including image management, registry security, runtime troubleshooting, and performance optimisation.
• Integrate and operate monitoring and telemetry systems, such as DCGM, Prometheus, node exporters, Weka telemetry, and alert pipelines.
• Drive continuous improvement in GPU utilisation efficiency, benchmarking, platform stability, and cost/performance optimisation.
• Own operational workflows including incident, problem, and change management, RCA execution, and improvement tracking.
• Lead capacity planning across compute, GPU, network, and storage layers to support scale-up and customer growth.
• Maintain complete system documentation including SOPs, runbooks, KB articles, architecture diagrams, configuration standards, and platform records.
• Oversee the ticketing lifecycle across internal operations, customer interfaces, and vendor escalation including RMA tracking and replacement management.
• Ensure strong SLA alignment and customer interaction through accurate troubleshooting and triage across GPU, Kubernetes, and OS environments.
• Support ISO27001 and SOC2 compliance through configuration standards, access controls, logging, vulnerability remediation, and platform security practices.
• Maintain audit readiness and evidence collection for operational and security compliance.
• Collaborate with vendors, partners, and engineering teams to resolve systemic GPU, container, or orchestration issues.
• Support budgeting and forecasting related to GPU expansion, licensing, storage growth, and platform evolution.

Skills and Experience

• Bachelor’s degree in computer science, Engineering, or related discipline.
• 15+ years’ experience in solution architecture, cloud engineering, HPC, or AI infrastructure.
• Deep hands-on experience with Linux systems, GPU platforms, Kubernetes orchestration, and container runtimes.
• Strong technical knowledge across drivers, firmware, OS tuning, and performance benchmarking.
• Practical experience supporting large-scale GPU clusters or HPC environments.
• Practical experience with monitoring and telemetry platforms such as DCGM, Prometheus, Grafana, and Weka.
• Good understanding of platform automation and infrastructure-as-code tooling (e.g., Ansible, Terraform).
• Strong knowledge of troubleshooting processes across complex stack layers (OS, container, GPU, network, storage).
• Excellent communication skills to work effectively across technical and non-technical stakeholders.
• Strong documentation discipline and ability to translate technical concepts into clear written content.
• Knowledge of ticketing platforms and RMA management processes in large-scale compute environments.
• Excellent documentation and diagramming abilities.
• Self-driven, analytical, and detail-oriented.