AI Infrastructure Engineer | Up to $7K - 0310

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 AI Infrastructure Engineer to design and operate high-density GPU compute clusters and optimize distributed AI workloads. You will ensure reliable performance and minimal latency across complex pipelines.

You will work with Kubernetes, Slurm, and Ray for orchestration, manage IaC with Terraform and Ansible, and fine-tune Linux, CUDA, and network stacks to maximize throughput in AI pipelines.

Qualifications

• Bachelor’s degree in CS, IT, or engineering or equivalent practical experience.
• 3–6+ years of hands-on infra/HPC/DevOps experience.
• Certs such as CKA/CKAD, NVIDIA certs or cloud architect certs are a plus.

Responsibilities

• Architect, configure, and maintain high-density multi-GPU compute clusters.
• Implement and manage container orchestration platforms optimized for AI workloads.
• Monitor GPU health, telemetry, utilization, and thermals.
• Lead incident response and disaster recovery planning for AI environments.

Skills

Linux administration

GPU compute / HPC

Kubernetes

Slurm / HPC schedulers

Ray

RDMA / InfiniBand networking

Terraform / Ansible / IaC

Bash / Python scripting

CUDA / NVIDIA GPUs

NVIDIA HGX/DGX architectures

Education

Bachelor’s Degree in Computer Science / Information Technology / Computer Engineering

Tools

Kubernetes

Slurm

Ray

Terraform

Ansible

Helm

Pulumi

Lustre / GPFS

Ceph

NVMe-oF

Job description

AI Infrastructure Engineer

• 5 days, Mon - Fri 8.30am to 5.30pm
• Salary: $5,000 to $7,000
• Location:Kaki Bukit
• 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).

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.