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