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 seeks an experienced Compute & Cluster Management specialist to architect, configure, and maintain high-density multi-GPU compute clusters and to implement container orchestration for AI/ML workloads. You will monitor GPU health, optimize performance, and collaborate with AI/ML teams to remove bottlenecks.

The ideal candidate has 3–6+ years in infrastructure engineering/HPC/DevOps, strong Linux expertise, GPU architectures, RDMA networking, and IaC

Qualifications

• Bachelor’s degree in CS/IT/CE or equivalent is required.
• 3–6+ years in infrastructure engineering, HPC, DevOps, or cloud infra.
• Deep Linux expertise, kernel tuning, and scripting.
• Experience with GPU HPC, RDMA networking, and container orchestration.

Responsibilities

• Architect, configure, and maintain high-density multi-GPU compute clusters.
• Implement and manage container orchestration platforms (Kubernetes, Slurm, Ray).
• Monitor GPU health and I/O, optimize performance, and prevent bottlenecks.
• Design and scale storage, networking, and IaC pipelines (Terraform/Ansible).

Skills

Compute cluster management

GPU hardware

Kubernetes

Slurm

Ray

NCCL

HPC

Terraform

Ansible

Pulumi

Linux systems

CI/CD

Education

Bachelor's degree in CS/IT/CE

Tools

Kubernetes

Slurm

Ray

Terraform

Ansible

Helm

Pulumi

NVIDIA GPU

Prometheus

Grafana

Job description

5 days, Mon - Fri 8.30am to 5.30pm

Salary: $5,000 to $7,000

Location:2 Kaki Bukit Ave 1, Singapore 417938

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).