6723 - 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. in Singapore is seeking an AI Infrastructure Engineer to architect and maintain high-density GPU compute clusters for AI/ML workloads.

You will optimize Kubernetes, Slurm, and Ray orchestration, while ensuring low-latency networking and fast data access. Your role includes building IaC pipelines with Terraform and Ansible, tuning Linux systems, and collaborating with AI/ML teams to resolve bottlenecks using CUDA/NCCL and HPC tooling.

Qualifications

• Deep Linux systems administration, kernel tuning, and shell scripting experience.
• Strong GPU hardware knowledge, CUDA runtimes, and PCIe/NVLink topology awareness.
• Hands-on Kubernetes (GPU operator) and HPC schedulers (Slurm, Run:AI, Ray).
• Experience with RDMA networks (RoCE v2/InfiniBand) and ECN/PFC settings.
• Familiar with shared storage architectures for AI datasets and checkpoints.
• IaC (Terraform) and configuration management (Ansible) proficiency.
• Bachelor's degree and 3–6+ years in infrastructure engineering or HPC.

Responsibilities

• Architect, configure, and maintain high-density multi-GPU compute clusters.
• Implement and manage container orchestration platforms optimized for AI workloads.
• Monitor GPU health, utilization, and prevent bottlenecks; optimize I/O.
• Design low-latency networks and scale parallel file systems for AI data.
• Build automated deployment pipelines using Terraform, Ansible, and similar tools.
• Lead incident response and RCA for mission-critical AI environments.

Skills

Linux systems administration

GPU hardware architectures

Kubernetes

Slurm

Ray

Terraform

Ansible

Bash

Python

CUDA / cuDNN

NVIDIA HGX / DGX

NCCL

Education

Bachelor's Degree in Computer Science, Information Technology, Computer Engineering, or equivalent

Tools

Terraform

Ansible

NVIDIA DGX tooling

RDMA tooling

Job description

AI Infrastructure Engineer

5 days, Mon - Fri 8.30am to 5.30pm

Salary: $5,000 to $7,000

Location:Kaki Bukit Ave

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).