System Engineer – Infrastructure (AI & HPC Systems)

We are looking for a hands-on System Engineer – Infrastructure to support and optimise large-scale GPU and CPU infrastructure powering AI workloads, large model training and high-performance computing environments.

You will be responsible for deploying, maintaining and troubleshooting GPU/CPU servers while ensuring infrastructure reliability, performance and operational readiness.

Key Responsibilities

• Deploy, configure and maintain GPU and CPU servers across large-scale compute clusters.
• Optimise BIOS, firmware and operating system configurations for AI and HPC workloads.
• Perform server health monitoring, hardware diagnostics, firmware upgrades and lifecycle management.
• Support cluster deployments across multiple racks and coordinate with hardware vendors and system integrators.
• Manage server provisioning, OS deployment, GPU/NIC driver installation and system hardening.
• Conduct system validation, burn-in testing and workload benchmarking.
• Monitor system health, investigate failures and perform root cause analysis.
• Work closely with networking, storage and DevOps teams to ensure end-to-end infrastructure performance.
• Develop scripts and automation to improve infrastructure deployment, monitoring and remediation.
• Maintain technical documentation including system configurations, rack layouts, cabling and operational procedures.
• Provide L2/L3 support and participate in on-call activities.

Requirements

• Bachelor's degree in Computer Science, Electrical Engineering or a related technical field.
• At least 3 years of hands-on experience managing server infrastructure in HPC, AI, GPU cluster or data centre environments.
• Strong experience with Linux systems, system tuning and performance optimisation.
• Hands-on experience with GPU/CPU servers and bare-metal infrastructure.
• Knowledge of server provisioning technologies such as IPMI, PXE, Redfish or BMC.
• Familiarity with monitoring tools such as Prometheus and Grafana.
• Basic knowledge of Kubernetes or containerised environments.
• Experience with server hardware, GPU platforms and infrastructure troubleshooting.