AI System Architect - Transport AI Programme

About the role

The Land Transport Authority (LTA) is investing in AI capabilities to improve how Singapore's land transport network is sensed, managed and optimised. The AI System Architect defines and owns the technical architecture of the Transport AI Programme, setting architectural standards, guiding engineering decisions and acting as the technical bridge between the Programme Director's strategy and the engineering team. The role reports to the Transport AI Programme Director and coordinates with industry partners and cross-government digital platforms.

Key responsibilities

• Define and maintain the end-to-end technical architecture, covering model serving, data platforms, integration layers, AI safety infrastructure and observability.
• Establish architectural patterns and standards for generative AI, agentic workflows, RAG systems, computer vision pipelines and shared AI platforms.
• Lead technical due diligence and architecture reviews of industry partner solutions against LTA's standards for security, privacy, resilience and operational readiness.
• Design AI infrastructure supporting automated evaluations, risk-posture dashboards, model drift monitoring and deployment playbooks.
• Work with data governance, security and policy teams on personal data protection, prompt-injection defence and responsible AI.
• Mentor AI software developers and infrastructure engineers and act as the main technical interface with external partners.
• Contribute to whole-of-government AI platform discussions and align LTA's architecture with national digital infrastructure.

Required profile

• Computer Science, Computer Engineering or a related degree, with 3-6 years of professional experience in software or systems architecture, at least 3 years focused on AI or data-intensive systems; candidates are assessed on experience and may be considered at intermediate or senior level.
• Experience designing systems in regulated or high-availability environments.
• Prior experience in government, transport or critical infrastructure is an advantage.
• Strong communication skills to explain technical concepts to engineers and non-technical stakeholders, and comfort in cross-functional, Agile teams.

Required skills

• Modern AI platform components: LLM serving infrastructure, vector databases, orchestration frameworks and MLOps tooling.
• Cloud-native architecture, API design and enterprise integration patterns.