[What the role is]

[What you will be working on]

Singapore's land transport network moves millions of people every day. As AI reshapes what's possible in transport operations, the Land Transport Authority is investing in AI capabilities that will meaningfully improve how the network is sensed, managed, and optimised — for commuters, operators, and public officers alike.

You will build and operate the cloud and on-premise infrastructure that underpins LTA's AI systems, ensuring that AI workloads run reliably, securely, and at scale. You will work closely with AI developers and architects to provision, automate, and maintain the environments in which AI models are trained, evaluated, and served — including in operationally sensitive transport contexts.

This role reports to the Transport AI Programme Director and involves close 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 (IaC) configurations, and automated deployment workflows for AI systems.

• Implement and manage observability tooling — including logging, monitoring, alerting, and tracing — across AI services in production.

• Ensure infrastructure meets security, compliance, and data residency requirements relevant to Singapore Government systems, including access controls, encryption, and audit logging.

• Support MLOps workflows including model registry management, experiment tracking, and automated evaluation pipelines.

• Collaborate with industry partners on infrastructure integration, managing environment parity, access provisioning, and deployment coordination across organisational boundaries.

• Contribute to incident response, capacity planning, and disaster recovery planning for AI systems supporting live transport operations.

[What we are looking for]

• Knowledge in Computer Science, Computer Engineering or a related field, with at least 2-4 years infrastructure or DevOps/MLOps engineering experience, with demonstrated experience supporting AI or data-intensive workloads. Candidates will be assessed based on their experience and may be considered at the intermediate or senior level.

• Proficiency with at least one major cloud platform (AWS, Azure, or GCP) and infrastructure-as-code tools (Terraform, Pulumi, or equivalent) is expected. Experience with Kubernetes, container orchestration, and GPU infrastructure management is highly desirable.

• Familiarity with MLOps platforms (such as MLflow, Kubeflow, or SageMaker) and AI observability tooling is a strong advantage.

• Experience working in secure or regulated environments, particularly within government or critical infrastructure is a plus.

• Comfortable working in cross-functional teams and collaborating with both technical and non-technical stakeholders to translate operational data needs into robust engineering solutions.

• Strong communication skills to explain technical concepts to both engineers and non-technical stakeholders. Inclination to work in a collaborative, fast-moving Agile environment. Strong in communication with the ability to explain complex technical concepts clearly to diverse audience

As part of the shortlisting process for the role, you may be required to complete a medical declaration and / or undergo further assessment.