AI Platform & DevOps Engineer

About the role

Sembcorp, an energy and urban solutions provider headquartered in Singapore, is looking for an AI Platform & DevOps Engineer to build and operate shared platforms supporting enterprise data and AI applications. The role is hands-on, focused on Azure, deployment automation, monitoring, security and cost control, and works with Data Engineers, Infrastructure, Cyber Security and application teams to bring solutions into production.

Key responsibilities

• Build and maintain cloud and Kubernetes environments for data and AI workloads, partnering with Infrastructure on networking, identity, storage and capacity.
• Automate infrastructure provisioning and deployments through infrastructure as code, CI/CD pipelines and reusable templates.
• Implement monitoring and alerting, investigate production incidents and resolve recurring reliability, performance and capacity issues.
• Support Databricks platform operations, including workspace configuration, access, compute policies and cost controls.
• Build and operate AI gateway capabilities for model access, authentication, routing, usage limits and token and cost monitoring.
• Implement security controls with Infrastructure and Cyber Security, including secrets management, least-privilege access, vulnerability remediation and hardening.
• Maintain runbooks, recovery procedures and operational documentation, and support recovery testing and production readiness.
• Evaluate new platform capabilities and introduce those that improve reliability, delivery speed or cost.

Required profile

• Typically 6 or more years in platform engineering, DevOps, cloud engineering or site reliability engineering, with hands-on production ownership.
• Strong practical experience with Microsoft Azure, Docker and Kubernetes or AKS.
• Experience building CI/CD pipelines with Azure DevOps, GitHub Actions or equivalent tools, and using Git for version control.
• Practical infrastructure-as-code experience, such as Terraform or Bicep, and scripting with Python or Bash.
• Working knowledge of cloud networking, identity and access management, secrets, observability and container security.
• Evidence of troubleshooting production issues, implementing lasting fixes and collaborating across engineering and security teams.

Required skills

• Databricks administration, compute policies and platform monitoring.
• AI gateways, model serving, MLOps or AI usage and cost controls.
• MCP integrations, AI agents and LLM evaluation using tools such as MLflow, DeepEval or equivalent.
• GCP integrations or hybrid-cloud environments.
• GPU inference operations using NVIDIA infrastructure, vLLM or equivalent.