DevOps Engineer, GPUaaS

Singtel Group; Singapore; On-site; SGD 60,000 - 90,000; Full time

Benefits offered by this job

Full suite of health and wellness benefits

Ongoing training and development programs

Internal mobility opportunities

Job summary

A leading telecommunications company in Singapore is seeking a DevOps Engineer to enhance its GPU-as-a-Service (GPUaaS) operations. The role involves designing and supporting GPU clusters for AI workloads, implementing CI/CD pipelines, and troubleshooting compute resources. Candidates should possess a Bachelor's degree in a related field and experience with DevOps tools like Jenkins and Kubernetes. This position offers health benefits and opportunities for career growth in a dynamic environment.

Qualifications

• Experience with implementing CI/CD pipelines.
• Strong understanding of automation and monitoring solutions.
• Knowledge of AI frameworks such as TensorFlow and PyTorch.

Responsibilities

• Design and support large-scale GPU clusters for AI and ML workloads.
• Implement CI/CD pipelines for applications.
• Troubleshoot system-level compute resource issues.

Skills

Experience with DevOps tools such as Jenkins

Solid understanding of DevOps practices

Proficiency in scripting languages

Strong technical problem solving

Team player with cross‑functional coordination

Education

Bachelor’s degree in Computer Science/Engineering

Tools

Kubernetes

Terraform

Ansible

Python

Bash

Zabbix

Prometheus

Job description

Singtel Digital InfraCo’s RE:AI division is building Asia’s most advanced and sustainable AI infrastructure ecosystem. RE:AI enables enterprises, research institutions, and digital-native businesses to accelerate innovation through responsible, high-performance AI compute and connectivity solutions.

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. T his role is suitable for professionals looking to develop their expertise in DevOps and AI/HPC cloud platforms.

Responsibilities

• Design, deploy and support large-scale, distributed 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 keep 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 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.

Requirements

• Bachelor’s degree in Computer Science/Engineering, Information Technology, Systems Engineering, or a related field.
• 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.
• 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.

Rewards that Go Beyond

• Full suite of health and wellness benefits.
• Ongoing training and development programs.
• Internal mobility opportunities.

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