Senior/Executive Infrastructure Engineer - Transport AI Programme

About the role

The Land Transport Authority (LTA) is building AI capabilities to improve how Singapore's land transport network is sensed, managed and optimised. This role builds and operates the cloud and on-premise infrastructure that underpins LTA's AI systems, working closely with AI developers and architects to provision, automate and maintain the environments where AI models are trained, evaluated and served. It reports to the Transport AI Programme Director and involves collaboration with industry partners on infrastructure integration and joint deployment.

Key responsibilities

• Provision, configure and manage cloud and hybrid infrastructure for AI workloads, including GPU compute, model serving environments, data storage and networking.
• Build and maintain CI/CD pipelines, infrastructure-as-code configurations and automated deployment workflows.
• Implement observability tooling (logging, monitoring, alerting, tracing) across AI services in production.
• Ensure infrastructure meets security, compliance and data residency requirements for Singapore Government systems, including access controls, encryption and audit logging.
• Support MLOps workflows such as model registry management, experiment tracking and automated evaluation pipelines.
• Collaborate with industry partners on environment parity, access provisioning and deployment coordination.
• Contribute to incident response, capacity planning and disaster recovery planning for AI systems supporting live transport operations.

Required profile

• Computer Science, Computer Engineering or related field, with at least 2-4 years of infrastructure or DevOps/MLOps engineering experience supporting AI or data-intensive workloads; candidates are assessed on experience and may be considered at intermediate or senior level.
• Experience in secure or regulated environments, especially government or critical infrastructure, is a plus.
• Comfortable working in cross-functional teams and explaining technical concepts to engineers and non-technical stakeholders; inclined to work in a collaborative, fast-moving Agile environment.

Required skills

• Proficiency with at least one major cloud platform (AWS, Azure or GCP) and infrastructure-as-code tools (Terraform, Pulumi or equivalent).
• Highly desirable: Kubernetes, container orchestration and GPU infrastructure management.
• Strong advantage: familiarity with MLOps platforms (MLflow, Kubeflow, SageMaker) and AI observability tooling.