DevOps Engineer

Singtel; Singapore; On-site; Confidential; Full time

Job summary

Singtel is seeking a DevOps Engineer to design, deploy and operate GPUaaS infrastructure for AI/HPC workloads. The role spans on-prem and cloud resources, with a focus on automation, monitoring, and robust CI/CD pipelines for GPU-accelerated applications.

You will work with Slurm, Kubernetes, and GPU drivers, contributing to performance optimizations and scalable, secure multi-tenant environments. Strong Linux skills and scripting are essential.

Qualifications

• Bachelor’s degree in a relevant field.
• Strong Linux system administration.
• Experience with DevOps tools (Jenkins, Kubernetes, Ansible, Terraform).
• Strong CI/CD, automation, and monitoring skills.
• Scripting in Python or Bash.
• Experience with monitoring solutions like Zabbix, Prometheus.
• Familiarity with AI frameworks (TensorFlow, PyTorch).

Responsibilities

• Design, deploy and support large-scale GPU clusters for AI/ML workloads.
• Manage provisioning of GPU resources on-prem and cloud.
• Design and manage CI/CD pipelines for AI models and GPU apps.
• Monitor cluster usage, health, performance and availability.
• Automate infrastructure provisioning and monitoring.
• Troubleshoot Slurm, Kubernetes, GPU drivers, CUDA, IB networking.
• Set up monitoring/logging with Zabbix, Prometheus, DCGM.
• Provide technical support to users of GPU-accelerated systems.

Skills

Linux system administration

Jenkins

Kubernetes

Ansible

Terraform

CI/CD

Automation

Monitoring

Python

Bash

Zabbix

Prometheus

TensorFlow

PyTorch

Education

Bachelor’s degree in Computer Science/Engineering or related field

Tools

Docker

Slurm

NVIDIA DCGM

NVIDIA GPU drivers

Job description

Be a Part of Something BIG!

As an DevOps Engineer for SingTel’s GPU-as-a-Service (GPUaaS), you will help in implementing processes and integration of operations to advance customer’s AI and HPC capabilities. You will be exposed to both physical data center implementation and software solutions in a Singtel GPU-as-a-Service (GPUaaS). This position requires a forward-thinking individual who thrives in dynamic environments and is committed to driving continuous improvement in GPU for AI and HPC environments. This role is suitable for professionals looking to develop their expertise in DevOps and AI/HPC cloud platforms.

Make an impact by

• Design, deploy and support large-scale, distribute GPU clusters for AI and ML workloads.
• Manage and automate provisioning of GPU resources in both on-prem and cloud platforms.
• Design, implement and manage CI/CD pipelines for AI models and GPU-accelerated applications.
• Monitor cluster usage, health, performance and availability.
• Improve infrastructure provisioning, management, and monitoring through automation.
• Troubleshoot compute resource system level issues such as Slurm, Kubernetes, GPU drivers, CUDA, IB networking.
• Optimize system parameters (e.g., OS, drivers, networking, library) for AI workload performance.
• Conduct GPU cluster benchmark and keeping up with the latest advancements in GPU technology.
• Set up monitoring and logging for GPU resources using Zabbix, Prometheus, NVIDIA DCGM and other tools.
• Implement security best-practices for multi-tenant GPU-as-a-Service (GPUaaS) environment.
• Collaborate with software and administrator to to streamline workflows and improve collaboration.
• Providing technical support and guidance to users of GPU-accelerated systems.
• Work with senior DevOps engineer to identify bottlenecks and improve development and operational processes for AI and HPC GPU cloud.
• Learning to solve problems in high-performance distributed computation for AI and HPC GPU cloud computing.
• Participate in rotational or scheduled shift work as required to support platform operations.

Skills for success

• Bachelor’s degree in Computer Science/Engineering, Information Technology, Systems Engineering, or a related field.
• Strong Linux system administration skills in Ubuntu/CentOS/Rocky Linux, etc.
• Experience with DevOps tools such as Jenkins, Kubernetes, Ansible and Terraform.
• Solid understanding of DevOps practices, including CI/CD, automation, and monitoring.
• Proficiency in scripting languages (e.g., Python, Bash).
• Experience in implementing monitoring solutions such as Zabbix, Prometheus.
• Familiarity with AI frameworks such as TensorFlow, PyTorch.
• Understanding of cloud architectures (IaaS, PaaS), GPU architecture and NVIDIA GPUs.
• Strong verbal, written, and presentation skills in English.
• Team player with experience in cross-functional coordination.
• Strong technical problem solving and analytical skills for system optimization.

Desirable qualifications

• Understanding of how collective communications (MPI, RDMA, and NCCL) works, as well as an understanding of GPU specific acceleration works on GPU cluster.
• Knowledge of DevOps/ML Ops technologies in GPU cluster such as Docker/containers, Kubernetes, data center deployments
• Familiarity with Slurm or other HPC workload managers to manage GPU clusters.
• Understanding of AI & HPC networking technologies such as InfiniBand, RoCE, DPUs.
• System-level experience specifically GPU-based systems (NVIDIA GPU and SDKs)
• Understanding how AI and HPC workloads interact with both GPU HW and SW infrastructure.