AI 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. is seeking an AI Infrastructure Engineer to design and manage high-density GPU compute clusters, optimize AI/ML workloads, and ensure robust HPC networking.

You will work on Kubernetes, Slurm, and Ray deployments, plus storage backends like Lustre or Ceph, with Terraform/Ansible for IaC. Ideal candidates have 3–6+ years in infrastructure engineering, strong Linux expertise, GPU/AI hardware know-how, and relevant certifications.

Qualifications

• Bachelor's degree as stated.
• 3–6+ years of hands-on infrastructure/AI HPC experience.
• Certs such as CKA/CKAD or NVIDIA/AWS/GCP certs are a plus.

Responsibilities

• Architect and maintain high-density multi-GPU compute clusters.
• Implement container orchestration optimized for AI/ML workloads.
• Monitor GPU health, telemetry, and thermals to minimize idle compute time.
• Design low-latency, lossless networks for distributed training.
• Configure parallel file systems and object storage for data pipelines.
• Build automated deployment pipelines using Terraform, Ansible, Helm or Pulumi.
• Maintain golden images and Linux kernel tuning for performance.

Skills

Linux admin

Kernel tuning

Shell scripting

GPU/AI infra

HPC concepts

DevOps practices

CI/CD familiarity

Education

Bachelor’s degree in CS/IT/CE

Tools

Kubernetes

Slurm

Ray

CUDA/Runtimes

RDMA

Lustre

Ceph

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.

THE SUPREMEHR ADVISORY PTE LTDEANO:14C7279