6723 - AI Systems Engineer | Up to $7K | Kaki Bukit | Kubernetes, Linux & GPU Clusters

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 hiring an AI Infrastructure Engineer in Singapore. You will design and operate high-density GPU compute clusters, optimize distributed AI workloads, and implement automated deployment and monitoring pipelines.

You will work with Kubernetes-based GPU operators, Slurm, and Ray, ensuring low latency, high throughput, and robust disaster recovery for mission-critical AI environments.

Qualifications

• Bachelor’s Degree or equivalent practical experience in CS/IT/Engineering.
• 3–6+ years of hands‑on infra, HPC, DevOps, or cloud infra experience.
• Certs like CKA/CKAD or cloud architect certs are a plus.

Responsibilities

• Architect and maintain high-density multi-GPU compute clusters (NVIDIA HGX/DGX).
• Manage container orchestration platforms (Kubernetes, Slurm, Ray) for AI/ML workloads.
• Monitor GPU health, telemetry, utilization, and thermals; minimize idle time.
• Design low-latency networks and scalable high-throughput storage for AI data pipelines.
• Build automated deployment pipelines with Terraform/Ansible/Helm/Pulumi.
• Lead incident response and disaster recovery for AI environments.

Skills

Linux administration

GPU architectures

NVIDIA CUDA

Kubernetes

Slurm

Ray

Terraform

Ansible

Pulumi

NCCL latency

Education

Bachelor’s Degree in Computer Science/IT/Engineering

Tools

Kubernetes

Slurm

Ray

Terraform

Ansible

Helm

Pulumi

Prometheus

Grafana

NVIDIA DCGM exporter

Job description

AI Infrastructure Engineer

5 days, Mon - Fri 8.30am to 5.30pm

Salary: $5,000 to $7,000

Location:Kaki Bukit Ave

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).