AI Infrastructure Engineer | Up to $7000 | {hkhdv}

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 to architect and operate high-density GPU clusters and distributed AI workloads. You will implement container orchestration (Kubernetes, Slurm, Ray), optimize kernel parameters, and ensure low-latency networking and fast storage for training pipelines.

You will collaborate with AI/ML teams, monitor GPU health and NCCL latency, and drive automation via Terraform and Ansible to enable reliable, scalable AI infrastructure.

Qualifications

• Bachelor's degree in CS/IT/CE or equivalent.
• 3–6+ years hands-on experience in infrastructure engineering, HPC, DevOps, or cloud infra.
• Deep Linux administration, kernel tuning, and scripting (Bash/Python).
• Strong understanding of GPU hardware architectures, CUDA runtimes, and PCIe/NVLink topologies.
• Hands-on experience with Kubernetes GPU operators and HPC schedulers (Slurm, Run:ai, Ray).
• Proven experience with RDMA (RoCE v2 /InfiniBand), PFC, ECN.
• Familiarity with high-IOPS, low-latency shared storage for AI datasets

Responsibilities

• Architect, configure, and maintain AI compute clusters.
• Implement and manage container orchestration platforms optimized for AI/ML workloads.
• Monitor GPU health, telemetry, utilization and thermals; minimize idle compute time.
• Design and optimize low-latency networks and scalable storage for AI pipelines.
• Build automated deployment pipelines using Terraform and Ansible; maintain golden images.
• Lead incident response, RCA, and disaster recovery for AI environments.

Skills

Linux administration

Kernel tuning

Shell scripting

GPU architectures

CUDA runtimes

PCIe/NVLink

Kubernetes

Slurm

Run:AI / Ray

NVIDIA HGX/DGX

RDMA / InfiniBand

Storage for AI data

Terraform

Ansible

Monitoring tooling

Education

Bachelor’s Degree in Computer Science / IT / Computer Engineering

Tools

Kubernetes GPU Operator

Slurm HPC Scheduler

Ray

Terraform

Ansible

Pulumi

nvidia-smi / CUDA toolkit

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:

• 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).