New opportunity

Senior Engineer - Compute, Container & AI Infrastructure

Singtel Group · Singapore

About this role

Singtel is seeking a Senior Engineer to join the Infrastructure Engineering team to design, operate, and enhance enterprise AI infrastructure. The role focuses on leveraging Red Hat OpenShift AI, Kubernetes, and DevSecOps practices to enable container and VM workloads at scale while ensuring platform reliability, security, and automation. Responsibilities include: Platform Engineering: Administering Red Hat OpenShift Container Platform (OCP) environments, managing cluster health, networking, storage, and security. Supporting Hosted Control Plane (HyperShift) and multi-cluster integrations. GitOps & Automation: Implementing GitOps-based deployments using Argo CD, managing CI/CD pipelines, and driving platform automation via Infrastructure-as-Code using Ansible, Python, or Bash. Infrastructure & GPU Engineering: Managing x86 server infrastructure and NVIDIA GPU servers for AI/ML workloads. Performing lifecycle management, capacity planning, and performance optimization for compute, storage, and GPU layers. Qualifications: Bachelor's degree in Computer Science, Engineering, or related field. Minimum 10 years of experience in platform or infrastructure engineering, with at least 5 years specifically managing Red Hat OpenShift in production. Strong background in Kubernetes, container technologies, and Linux/RHEL. Hands-on experience with NVIDIA GPU infrastructure and a solid understanding of AI/ML concepts such as model serving, inferencing, and RAG. Preferred qualifications include certifications such as RHCSA, RHCE, or CKA, and experience with OpenShift Service Mesh, KubeVirt, and AI security.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

Red Hat OpenShiftKubernetesAI InfrastructureDevSecOpsArgo CDGitOpsNVIDIA GPUMIGLinuxRHELAnsiblePythonBashIstioKubeVirtHyperShift

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Red Hat OpenShift Platform Engineering
The role requires deep expertise in managing enterprise-grade OpenShift Container Platform (OCP) environments. You must demonstrate proficiency in cluster health, lifecycle management, networking, and storage, specifically supporting advanced features like Hosted Control Plane (HyperShift) and OpenShift Virtualization at scale.
NVIDIA GPU and AI Infrastructure Management
You are expected to manage the physical and logical layers of AI infrastructure, including x86 server hardware and NVIDIA GPU clusters. This involves performance optimization, capacity planning, and managing specialized configurations like Multi-Instance GPU (MIG) to support AI/ML model serving and inferencing workloads.
GitOps and DevSecOps Automation
The position mandates the implementation of GitOps workflows using Argo CD and robust CI/CD pipelines. You must prioritize the automation of platform operations using Infrastructure-as-Code (Ansible, Python, or Bash) while embedding security scanning and policy enforcement into the deployment lifecycle.

Likely challenges to think through

Balancing Performance with AI Workload Density
Optimizing GPU and compute resources for diverse AI/ML workloads can lead to resource contention. Prepare by discussing how you monitor and tune infrastructure to ensure high availability for model inferencing without compromising the stability of the underlying OpenShift platform.
Managing Complexity in Multi-Cluster Environments
Operating multi-cluster OpenShift environments with integrated DNS, identity, and security services introduces significant configuration drift risks. Prepare by highlighting your experience with centralized management strategies and how you maintain consistency across disparate clusters using GitOps and automation.

Practice your answers

Technical practice

Managing Red Hat OpenShift at Scale

What to coverDiscuss your experience with multi-cluster management and cluster health monitoring. Explain how you handle complex upgrades, node management, and troubleshooting networking or storage issues in a production environment.

NVIDIA GPU Infrastructure and MIG

What to coverDescribe your process for managing NVIDIA GPU servers, including the use of the GPU Operator. Explain how you implement Multi-Instance GPU (MIG) to optimize resource allocation for AI/ML workloads.

GitOps and Argo CD Implementation

What to coverExplain how you utilize Argo CD to enforce GitOps practices for platform and application deployments. Discuss how you manage configuration drift and ensure consistency across multiple OpenShift environments.

AI/ML Workload Optimization

What to coverDetail your understanding of model serving and inferencing within a containerized environment. Explain how you handle the specific infrastructure requirements for RAG (Retrieval-Augmented Generation) pipelines.

Automation with Infrastructure-as-Code

What to coverProvide examples of how you have used Ansible, Python, or Bash to automate platform operations. Focus on the creation of reusable deployment templates and the management of configuration-as-code.

Hosted Control Plane (HyperShift) Architecture

What to coverExplain the benefits of using HyperShift for managing control planes. Discuss how you handle lifecycle management for HostedClusters and NodePools in a large-scale deployment.

OpenShift Virtualization and KubeVirt

What to coverDescribe your experience running VM workloads on OpenShift using KubeVirt. Discuss the trade-offs between containerized and virtualized workloads in an enterprise infrastructure context.

DevSecOps and Platform Security

What to coverDetail your approach to securing container platforms, including vulnerability scanning, secrets management, and RBAC policies. Explain how you integrate security into CI/CD pipelines.

Behavioural practice

Handling Critical Production Outages

What to coverDescribe a time you had to troubleshoot a complex, high-impact infrastructure failure in a Kubernetes or OpenShift environment. Focus on your diagnostic process, communication with stakeholders, and the steps taken to restore service.

Managing Vendor and Stakeholder Relationships

What to coverProvide an example of how you managed a hardware refresh or infrastructure upgrade project involving external vendors. Explain how you balanced technical requirements with business constraints and SLA compliance.

Driving Automation Culture

What to coverShare a situation where you identified a manual, repetitive process and implemented an automated solution. Explain how you gained buy-in from your team and the long-term impact on operational efficiency.

Adapting to New AI Infrastructure Requirements

What to coverDescribe a scenario where you had to quickly learn and implement a new technology, such as a specific AI/ML tool or GPU configuration. Explain how you ensured the new infrastructure met performance and reliability standards.

Sharpen your knowledge

Topics to prepare

AI Infrastructure Architecture
Review the integration of Red Hat OpenShift AI (RHOAI) with Kubernetes to support model serving and inferencing. Exercise: Design a conceptual architecture diagram showing how RAG workloads interact with GPU-accelerated nodes in an OpenShift environment. This is relevant as the role requires managing AI-specific infrastructure at scale.
GitOps and Platform Automation
Study the principles of GitOps using Argo CD for declarative infrastructure management. Exercise: Create a mock repository structure that uses Argo CD to synchronize OpenShift cluster configurations and application deployments. This is essential for the role's focus on automating platform operations and CI/CD pipelines.
GPU Resource Management
Understand the NVIDIA GPU Operator and Multi-Instance GPU (MIG) partitioning within Kubernetes. Exercise: Explain how MIG allows multiple AI workloads to share a single physical GPU and how to monitor these resources via OpenShift. This is critical for the role's responsibility in optimizing GPU performance for AI/ML workloads.

Skills to revise

Red Hat OpenShift Administration
Practice advanced OCP troubleshooting, including node management, networking, and storage configuration. Exercise: Simulate a cluster upgrade or a node failure recovery scenario using OpenShift CLI tools. This directly addresses the requirement for 5+ years of production-level OCP experience.
Infrastructure-as-Code (Ansible/Bash)
Develop automation scripts for routine infrastructure tasks. Exercise: Write an Ansible playbook to automate the configuration of RHEL nodes or the deployment of a standard application stack on OpenShift. This is vital for the role's mandate to drive platform automation.
OpenShift Virtualization (KubeVirt)
Gain familiarity with running VM workloads alongside containers using KubeVirt. Exercise: Configure a virtual machine instance within an OpenShift cluster and define its networking and storage requirements. This is a preferred skill for managing hybrid container and VM workloads.

Your preparation checklist

Prepare a Red Hat OpenShift production case study
Draft a detailed walkthrough of a complex OpenShift production issue you resolved. Focus on your experience with cluster health, networking, storage, or security, and highlight how you managed multi-cluster environments or upgrades.
Demonstrate GPU infrastructure expertise
Prepare to discuss your hands-on experience managing NVIDIA GPU servers. Be ready to explain your process for capacity planning, performance optimization, and implementing Multi-Instance GPU (MIG) for AI/ML workloads.
Showcase GitOps and automation workflows
Create a summary of how you have implemented GitOps using Argo CD and CI/CD pipelines. Provide examples of how you used Ansible, Python, or Bash to automate platform operations and enforce DevSecOps standards.
Formulate technical questions for the team
Prepare questions regarding their current implementation of HyperShift, their strategy for AI security within RHOAI, or how they handle hardware lifecycle management for GPU-accelerated clusters at scale.
Review AI/ML infrastructure concepts
Refresh your knowledge on model serving, inferencing, and RAG architectures as they relate to containerized environments. Be prepared to discuss how you support these workloads on OpenShift.

Availability last checked: 18 Oct 2026. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

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