Editorial summary

The company is seeking an AI Infrastructure Engineer to manage and maintain high-density multi-GPU compute clusters and container orchestration platforms optimized for AI and machine learning workloads. The role involves monitoring GPU health, optimizing high-performance networking and storage, and ensuring efficient cluster operation. Candidates should have experience with GPU architectures such as NVIDIA HGX/DGX and familiarity with orchestration tools like Kubernetes, Slurm, or Ray. The position offers a salary range of $5,000 to $7,000 and is based at the company's location in Singapore, with a standard Monday to Friday work schedule.

This summary is AI-generated and may contain inaccuracies. Please refer to the full job description below.

Job description

[This job id 27298 first appeared in Job-Q.com on 24 Aug 2026]

AI Infrastructure Engineer

5 days, Mon - Fri 8.30am to 5.30pm

Salary: $5,000 to $7,000

Location: 2 Kaki Bukit Ave 1, Singapore 417938

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 data pipelines.

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).

If you are keen to apply, please send me your resume and job applied for on WhatsApp at 9789 3505 or email me at (˶ᵔ ᵕ ᵔ˶)

❄️Anabel Boon Xue Qi | Recruitment Consultant (R25159272) |📍The Supreme HR Advisory EA No: 14C7279

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