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Open nowPosted 17 days ago

AI Infrastructure Engineer

MyCareersFuture94,028 open roles

Pay
SGD 5,700 – SGD 6,400 a month
Where
Central, Singapore
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Your applicationOpen nowAI Infrastructure EngineerMyCareersFuture · Central, Singapore

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Early applications get read.

7.7% of postings close within 7 days. Measured by our own scanner across the market.

Share of postings closed within
  1. 1.6%1 day
  2. 3.3%3 days
  3. 7.7%7 days
  4. 14.0%14 days
  5. 33.7%30 days
This job: posted 17 days ago

The posting

1. Compute & ClusterManagement

  • 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.

2. 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.

3. 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.

4. 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.

Qualifications &Requirements

Technical Competencies

  • 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).

Experience & Education

  • 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).
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