Engineer / Senior Software Engineer, AI Products & Platform, Ministry of Education

Requirements

• Experience building production LLM or AI-enabled applications.
• Experience with evaluation frameworks, prompt/context engineering, model monitoring, Langfuse/OpenTelemetry-style tracing, or AI safety guardrails.
• Experience with Python, TypeScript, Go, PostgreSQL, containerised deployment, cloud servic

Benefits

Additional Information

GovTech is the lead agency driving Singapore's Smart Nation initiatives and public sector digital transformation. As the Centre of Excellence for Infocomm Technology and Smart Systems (ICT & SS), GovTech develops the Singapore Government's capabilities in Data Science & Artificial Intelligence, Application Development, Smart City Technology, Digital Infrastructure, and Cybersecurity. At GovTech, we offer you a purposeful career to make lives better where we empower our people to master their craft through robust learning and development opportunities all year round. Play a part in Singapore's vision to build a Smart Nation and embark on your meaningful journey to build tech for public good. Join us to advance our mission and shape your future with us today! Learn more about GovTech at tech.gov.sg. [About the Team] The Ministry of Education is building AI products and shared platform capabilities that can improve how students learn, how teachers work, and how HQ teams run complex operational workflows. The AI Platform & Capability Programme sits at the centre of this work. We build and support AI-native products such as learning assistants, marking tools, procurement agents, multimodal prototypes, and the shared evaluation, observability, model-access, and governance capabilities needed to scale them responsibly. We are hiring software engineers who can take ambiguous problems from brief to production. This is not a pure research role and not a demo-building role. The work is to build reliable, maintainable, policy-sensitive AI systems that can survive real users, real operational constraints, and public-sector trust requirements. AI will support how we build. Engineers still own the design, judgement, quality, and consequences of what ships. How we build We prefer simple, durable engineering choices over novelty for its own sake. The stack may vary by product, but the principles do not: clear architecture, automated testing, CI/CD, observability, secure-by-design delivery, and systems that are easy to reason about after launch. The team's operating model is product-led and platform-amplified. Product squads prove value in real education or HQ workflows; shared platform capabilities make those products observable, testable, governable, and reusable across MOE. Engineers may work inside a product squad, on the shared AI platform, or in a forward-deployed model where they help a vertical team build AI capability before bringing reusable patterns back to the centre [What you will be working on] Design, build, test, deploy, and operate AI-enabled products and platform components across MOE. Work on areas such as LLM integration, agentic workflows, evaluation harnesses, observability, guardrails, model access, retrieval/memory patterns, and multimodal AI use cases. Take ambiguous education or corporate operations problems and turn them into maintainable software with clear success metrics. Build production features for products such as procurement automation, teacher-facing AI tools, learning assistants, and evaluation/monitoring platforms. Create engineering patterns that other product teams can reuse, including templates, playbooks, test harnesses, and reference implementations. Work closely with product managers, designers, data scientists, governance colleagues, and business owners. Use AI coding and development tools to accelerate delivery while retaining human accountability for design and quality. Participate in design reviews, code reviews, incident response, and technical decision-making. Push for root-cause fixes rather than surface-level patches. [What we are looking for] Strong software engineering fundamentals: system design, testing, version control, CI/CD, observability, security, and maintainable code. Good engineering judgement: you can explain trade-offs clearly and defend a technical position without becoming attached to it. Comfort with ambiguity: you can move from unclear policy, education, or operational needs into a working product shape. Appetite for AI-native engineering: LLM applications, evaluation, agentic workflows, RAG/memory, multimodal models, or AI-assisted development. Pragmatic taste in technology: you choose boring, durable tools when they are right, and newer AI tooling only when it genuinely improves the system. Ownership mindset: you do not stop at prototype handoff; you care about deployment, monitoring, user feedback, and maintainability. Ability to work with non-technical stakeholders in sensitive public-sector domains. Bias for measurable quality: you care about evals, regression checks, traceability, and evidence before scaling.

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