6723 - GPU Infrastructure Engineer | Up to $7K | Kaki Bukit | NVIDIA, CUDA & HPC

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 seeking an AI Infrastructure Engineer in Singapore to architect and maintain high-density GPU compute clusters, optimize distributed AI workloads, and drive automation across deployment pipelines.

You will pair with AI/ML teams to monitor performance, scale storage, and ensure low-latency networking for distributed training. Minimum 3–6+ years in infra engineering, HPC, or DevOps is expected, with strong Linux, GPU, and Kubernetes expertise.

Qualifications

• Linux systems administration, kernel tuning, and shell scripting (Bash/Python).
• GPU hardware architectures, CUDA runtimes, and PCIe/NVLink knowledge.
• Kubernetes GPU operator and HPC schedulers (Slurm, Run:AI, Ray).
• High-speed networking: RDMA, RoCE v2, InfiniBand; storage for AI data.
• IaC: Terraform and configuration management with Ansible.
• Bachelor’s degree in CS/IT/Engineering or equivalent.
• 3–6+ years of hands‑on infra engineering, HPC, DevOps, or cloud infra.
• Certifications such as CKA/CKAD, NVIDIA certs, AWS/Azure/GCP are a plus.

Responsibilities

• Compute & Cluster Management: Architect, configure, and maintain high-density multi-GPU compute clusters (NVIDIA HGX/DGX).
• Container orchestration: Implement and manage Kubernetes, Slurm, or Ray optimized for AI/ML workloads.
• Monitor GPU health, telemetry, utilization, and thermals; minimize idle compute time and prevent bottlenecks.
• High-Performance Networking & Storage: Design and optimize low-latency fabrics and scalable storage (Lustre, Ceph, MinIO).
• Automation & IaC: Build and manage deployment pipelines using Terraform, Ansible, Helm, or Pulumi; maintain golden images and kernel tuning.
• Operations, Observability & Performance: Set up monitoring dashboards (Prometheus, Grafana, DCGM); diagnose bottlenecks; lead RCA and DR activities.

Skills

Linux systems administration

Kernel tuning

Shell scripting (Bash/Python)

GPU compute understanding

Distributed computing concepts

Observability

Incident response

NVIDIA GPU internals

Education

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

Tools

Kubernetes

Slurm

Ray

Terraform

Ansible

Helm

Pulumi

Prometheus

Grafana

NVIDIA SMI/DCGM

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).