AI Infrastructure Engineer responsible for architecting and maintaining multi-GPU compute clusters, Kubernetes/HPC orchestration, high-performance networking (InfiniBand/RoCE), parallel storage systems, and IaC automation in an on-site Singapore role.
AI Infrastructure Engineer
5 days, Mon - Fri 8.30am to 5.30pm
Salary: $5,000 to $7,000
Location: 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).
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