AI Infrastructure Engineer — GPU & HPC Clusters

RUNSUN SERVICE PTE. LTD.; Singapore; On-site; SGD 120,000 - 180,000; Full time

Job summary

RUNSUN SERVICE PTE. LTD. in Singapore seeks an experienced AI System Engineer to design, deploy, operate, and optimize AI training clusters and GPU platforms. The role involves managing Linux systems, CUDA/NVIDIA drivers, and container orchestration to ensure high availability.

You will build and maintain scalable AI infrastructure, automate operations, and work with datacenter teams to resolve training environment issues, while supporting on-call rotations and occasional travel as required.

Qualifications

• Bachelor's degree or above in Computer Engineering, Electrical Engineering, Telecommunications, or related fields.
• 3+ years of Linux administration experience; strong knowledge of Ubuntu, Rocky Linux, and RHEL; familiarity with boot process, kernel, filesystems, and performance tuning.
• Ability to troubleshoot complex system issues independently.
• Experience with NVIDIA GPU products H100, H200, B200, B300, GB200 NVL72 and GB300 NVL72.
• Familiar with CUDA, NCCL, NV Link, NV Switch, GPU Direct RDMA.
• Understanding of distributed AI training architectures.
• Hands-on experience with Kubernetes; familiarity with Docker and Containerd; Helm knowledge.

Responsibilities

• Deploy and operate AI training and HPC clusters.
• Install, configure, and optimize operating systems on GPU servers.
• Manage cluster resources and capacity.
• Develop infrastructure automation tools and scripts.
• Build monitoring and observability platforms and ensure high availability.

Skills

Linux administration

GPU computing

Container orchestration

Scripting (Shell, Python)

Network troubleshooting

Education

Bachelor's degree in Computer/Electrical Engineering or related fields

Tools

Kubernetes

Docker

Containerd

NVIDIA CUDA

NCCL

NVIDIA drivers

Job description

RUNSUN SERVICE PTE. LTD. in Singapore seeks an experienced AI System Engineer to design, deploy, operate, and optimize AI training clusters and GPU platforms. The role involves managing Linux systems, CUDA/NVIDIA drivers, and container orchestration to ensure high availability.

You will build and maintain scalable AI infrastructure, automate operations, and work with datacenter teams to resolve training environment issues, while supporting on-call rotations and occasional travel as required.