AI Platform & DevOps Engineer
Sembcorp Group1 day ago
Singapore, SingaporeSenior
Responsibilities
- Build and maintain Azure cloud and Kubernetes environments supporting data and AI workloads.
- Automate infrastructure provisioning and application deployments using infrastructure-as-code, CI/CD pipelines, and reusable templates.
- Implement monitoring and alerting, investigate production incidents, and resolve recurring reliability, performance, and capacity issues.
- Operate the Databricks platform, including workspace configuration, access, compute policies, monitoring, and cost controls.
- Build and operate AI gateway capabilities for model access, authentication, routing, usage limits, and token and cost monitoring.
- Implement secrets management, least-privilege access, vulnerability remediation, hardening, and other security controls with Infrastructure and Cyber Security teams.
- Maintain runbooks, recovery procedures, operational documentation, and production readiness practices, including recovery testing.
- Evaluate and introduce platform capabilities that improve reliability, delivery speed, or cost efficiency.
Requirements
- Typically six or more years of experience 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 with Terraform, Bicep, or equivalent tools, plus scripting experience with Python or Bash.
- Working knowledge of cloud networking, identity and access management, secrets, observability, and container security.
- Demonstrated ability to troubleshoot production issues, implement lasting fixes, and collaborate across engineering and security teams.
- Experience administering Databricks, including compute policies and platform monitoring.
- Experience with AI gateways, model serving, MLOps, or AI usage and cost controls.
- Exposure to MCP integrations, AI agents, and LLM evaluation using MLflow, DeepEval, or equivalent technologies.
- Experience with GCP integrations or hybrid-cloud environments is relevant.
- Experience with GPU inference operations using NVIDIA infrastructure, vLLM, or equivalent technologies.