AB03 - AI Infrastructure Engineer

THE SUPREME HR ADVISORY PTE. LTD.; Singapore; On-site; SGD 56,000 - 78,000; Full time

Job summary

THE SUPREME HR ADVISORY PTE. LTD. is hiring an AI Infrastructure Engineer in Singapore to architect and maintain high-density GPU compute clusters and optimize AI/ML workloads. The role covers cluster design, network and storage optimization, and automation via IaC tools.

You will collaborate with AI/ML teams to diagnose bottlenecks, manage GPU drivers and CUDA libraries, and ensure reliability in distributed training environments.

Qualifications

• Linux systems administration expertise with kernel tuning and scripting.
• Hands-on GPU hardware knowledge and CUDA runtimes.
• Experience with GPU orchestration and HPC schedulers (Kubernetes GPU operator, Slurm, Ray).
• Networking and storage for AI data pipelines; RDMA/InfiniBand knowledge is a plus.

Responsibilities

• Architect, configure, and maintain high-density multi-GPU compute clusters.
• Implement and manage container orchestration platforms optimized for AI/ML workloads.
• Monitor GPU health, telemetry, utilization, and thermals; minimize idle compute time.
• Design and optimize low-latency networks and scale storage for AI datasets.
• Build automated deployment pipelines using Terraform, Ansible, Helm, or Pulumi.
• Lead incident response and DR planning for AI environments.

Skills

Linux admin

Kernel tuning

Shell scripting

GPU architectures

Kubernetes

Slurm

Ray

Terraform

Ansible

PCIe/NVLink

Education

Bachelor’s Degree in CS/IT/Engineering

Tools

Kubernetes

Slurm

Ray

Terraform

Ansible

NVIDIA GPUs / HGX

Job description

AI Infrastructure Engineer

5 days, Mon - Fri 8.30am to 5.30pm

Salary: $5,000 to $7,000

Location:2 Kaki Bukit Ave 1, Singapore 417938

Job scopes:

Compute & Cluster Management

• Architect, configure, and maintain high-density multi-GPU compute clusters (e.g. NVIDIA HGX/DGX architectures).


• Implement and manage container orchestration platforms (Kubernetes, Slurm, or Ray) optimized for AI/ML distributed workloads.


• Monitor GPU health, telemetry, utilization, and thermals; minimize idle compute time and prevent single-node bottlenecks.

High-Performance Networking & Storage

• Design and optimize low-latency, lossless network fabrics supporting distributed training (InfiniBand, RoCE v2, NVLink, spine-leaf topologies).


• Configure and scale high-throughput parallel file systems and object storage (e.g. Lustre, GPFS/IBM Spectrum Scale, Ceph, MinIO, NVMe-oF) to feed high-speed datapipelines.

Automation & Infrastructure as Code (IaC)

• Build and manage automated deployment pipelines using Terraform, Ansible, Helm, or Pulumi.


• Maintain standard golden images, Linux OS tuning (kernel parameters, NUMA node binding, GPU drivers, CUDA/cuDNN libraries), and firmware updates.

Operations, Observability & Performance

• Set up end-to-end monitoring, alerting, and metrics dashboards (Prometheus, Grafana, DCGM exporter, NVIDIA System Management Interface).


• Partner with AI/ML engineering teams to diagnose network bottlenecks, NCCL communication latency, and I/O wait states during distributed training jobs.


• Lead incident response, root-cause analysis (RCA), and disaster recovery plans for mission-critical AI environments.

Requirements:

• Operating Systems:Deep expertise in Linux systems administration, kernel tuning, and shell scripting (Bash/Python).


• Accelerated Compute:Strong understanding of GPU hardware architectures, CUDA runtimes, and PCIe/NVLink topologies.


• Orchestration & Workload Scheduling:Hands-on experience with Kubernetes (GPU operator, device plugins) and/or HPC schedulers (Slurm, Run:ai, Ray).


• High-Speed Networking:Proven experience with RDMA (RoCE v2 /InfiniBand), PFC (Priority Flow Control), and ECN configurations.


• Storage Systems:Familiarity with high-IOPS, low-latency shared storage architectures for AI datasets and model checkpoints.


• Automation:Proficiency in Infrastructure as Code (Terraform) and configuration management (Ansible).


• Bachelor’s Degree in Computer Science, Information Technology, Computer
  Engineering, or equivalent practical experience.


• 3–6+years of hands-on experience in infrastructure engineering, high-performance computing (HPC), DevOps, or cloud infrastructure.


• Relevant certifications are a plus (e.g., CKA/CKAD, NVIDIA Certified
  Associate/Professional, AWS/Azure/GCP Solutions Architect).