AI DevOps Engineer (Cloud Infrastucture)

The Supreme HR Advisory Pte Ltd; Singapore; On-site; SGD 57,000 - 77,000; Full time

Job summary

The Supreme HR Advisory Pte Ltd is seeking an AI Infrastructure Engineer to architect and operate high-density GPU compute clusters in a Singapore on-site setting. You will deploy and manage containerized AI/ML workloads using Kubernetes, Slurm, or Ray, while tuning Linux systems and GPUs for peak performance.

You will build automated pipelines with Terraform and Ansible, monitor system health with Prometheus/Grafana, and collaborate with AI/ML teams to minimize bottlenecks during distributed

Qualifications

• Proficient Linux systems administration, kernel tuning, and scripting (Bash/Python).
• Hands-on GPU HPC knowledge with CUDA and NVLink technologies.
• Experience with GPU container orchestration (Kubernetes) and HPC schedulers (Slurm, Run:ai, Ray).
• Familiar with high-speed networking and RDMA (RoCE v2/InfiniBand).
• Proficient in Terraform and Ansible for IaC and configuration management.

Responsibilities

• Architect, configure, and maintain high-density GPU compute clusters.
• Design and optimize low-latency networks and scalable storage.
• Build automated deployment pipelines using Terraform, Ansible, and Helm.
• Monitor performance, diagnose bottlenecks, and lead incident response.
• Collaborate with AI/ML teams on distributed training workloads.

Skills

Linux administration

Shell scripting

GPU/ HPC

Kubernetes

Networking (RDMA)

Terraform/Ansible

Education

Bachelor's degree in Computer Science/IT/Engineering

Tools

Kubernetes

Slurm

Ray

Terraform

Ansible

Ceph

Job description

AI Infrastructure Engineer

• 5 days, Mon - Fri 8.30am to 5.30pm

• Salary: $5,000 to $7,000

• Location:Kaki Bukit

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).