Cloud Engineer (ECM & AI Systems)

About the role

Design, deploy and operate the cloud infrastructure that runs our Enterprise Content Management (ECM) platform and our Artificial Intelligence / machine learning systems for enterprise clients across Singapore and the region. You will own infrastructure-as-code, CI/CD, observability and cost optimisation for both workloads.

Key responsibilities

• Build and maintain cloud infrastructure on AWS, Azure or GCP for ECM and AI workloads
• Deploy, scale and support ECM platforms (e.g. OpenText, Documentum, SharePoint, Alfresco) including storage tiering, indexing, search and content migration
• Provision and operate AI/ML infrastructure - GPU compute, model serving endpoints, vector databases and RAG document pipelines that feed off the ECM content repository
• Automate provisioning with Terraform, CloudFormation or Bicep and manage containerised workloads on Kubernetes / EKS / AKS
• Build and maintain CI/CD pipelines (GitLab CI, GitHub Actions, Azure DevOps) and MLOps tooling
• Set up monitoring, logging and alerting; respond to incidents and drive root cause analysis
• Harden environments for security and compliance - IAM, encryption, network segmentation, audit logging, data residency and PDPA requirements
• Optimise cloud spend and right-size resources across environments
• Work with solution architects, data scientists and client teams to translate requirements into resilient designs
• Document architecture, runbooks and handover material

About you

• Degree or diploma in Computer Science, Information Technology, Engineering or related field
• 3-5 years of hands-on cloud engineering experience
• Strong with at least one major cloud platform (AWS, Azure or GCP); certification is an advantage
• Solid infrastructure-as-code skills (Terraform preferred) and scripting in Python, Bash or PowerShell
• Working knowledge of Docker and Kubernetes
• Experience with Linux administration, networking fundamentals and relational or NoSQL databases
• Exposure to enterprise content management systems, document workflows or large-scale unstructured data storage
• Exposure to AI/ML deployment - model serving, GPU instances, vector databases or LLM/RAG pipelines
• Good understanding of cloud security best practices
• Strong troubleshooting skills and clear written and verbal communication

Benefits

• Hands-on work across both enterprise content platforms and modern AI infrastructure
• Certification sponsorship and a training budget
• Clear progression toward senior or cloud architect roles
• Flexible hybrid working arrangements
• Medical coverage and annual performance bonus