Lead AI Engineer (AI Capability Development & Infrastructure), DXD (Digital Excellence & Products Division), Ministry of Education
Non-Headquarters MOE1, Singapore
The Singapore Public Service
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"To Mould the Future of Our Nation"
At MOE, we are accelerating the responsible and meaningful use of AI to transform education. As part of our digital transformation, we aim to develop robust internal AI capabilities — from intelligent learning tools and content generation to scalable evaluation infrastructure that ensures high-performing, safe, and trustworthy systems.
As a Lead AI Engineer, you will drive the productisation and operationalisation of AI models used across the education system. You will lead technical implementation, system integration, and performance engineering — while strengthening our internal AI evaluation platform and tooling. A core part of this role involves raising the maturity of how we evaluate and monitor AI in production environments.
Our Team
You will be part of the Digital Excellence & Products Division (DXD), which leads MOE’s digital product development, innovation, and platform strategy. DXD works closely with curriculum, operations, and policy teams to design and deliver meaningful, scalable digital solutions across schools and HQ.
This role focuses on evaluating and optimising best-in-class generative AI and LLM solutions for education, and developing the infrastructure needed to deploy them safely and effectively. You will also contribute to building horizontal AI evaluation capabilities, including benchmarking frameworks, observability tools, and CI/CD-integrated safeguards.
You will help build infrastructure that supports real-time LLM integrations across platforms used by over 30,000 educators and 350 schools — where reliability, performance, and trust are non-negotiable.
What you will be working on
AI Productisation and System Integration
Design and deploy scalable AI pipelines that support the real-time integration of LLMs and ML models into MOE’s digital platforms.
Work closely with product managers and AI scientists to move prototypes into production-ready solutions.
Ensure systems are robust, maintainable, and tuned for performance, latency, and cost-effectiveness.
Optimisation and Evaluation Infrastructure
Lead efforts to optimise models for inference speed, memory usage, and production readiness.
Design and implement tools for tracing, logging, and runtime monitoring of AI behaviour in live systems.
Partner with AI scientists to implement offline and online benchmarking, tracking regressions and surfacing performance gaps.
Strengthen Evaluation Infrastructure and Maturity
We are building a more systematic and trusted evaluation ecosystem to ensure the quality, safety, and effectiveness of AI models deployed in education. This includes:
Transitioning towards structured, automated evaluations
Implementing real-time monitoring, regression detection, and online metrics
Embedding evaluations into CI/CD pipelines, with safeguards to prevent unintended issues in production
You will lead the development of tooling, infrastructure, and workflows that raise the maturity of how we assess model performance and reliability — from early testing through to deployment and live operations.
Engineering Culture and Tooling
Establish best practices in model deployment, testing, and observability.
Contribute to reusable components, libraries, and infrastructure that support other AI and product teams.
Mentor engineers on best practices for AI evaluation, model reliability, and technical excellence.
What we are looking for
Engineering Leadership – Lead implementation across model deployment, observability, and infrastructure design with a focus on reliability and performance.
Performance and Optimisation – Improve model inference speed and cost efficiency while ensuring high-quality outputs.
Evaluation and Automation – Advance internal capability to evaluate and monitor AI models at scale, from structured testing to real-time online metrics.
Tooling and CI/CD Integration – Build automated systems that enable model testing, regression detection, and seamless rollout within dev pipelines.
Collaboration and Execution – Work closely with AI scientists, platform engineers, product managers, and data teams to deliver real-world, production-ready solutions.
Required Qualifications and Experience
Bachelor’s degree in Computer Science, Engineering, or a related technical field; Master’s preferred.
At least 5–8 years of experience in software engineering, machine learning engineering, or AI infrastructure roles.
Experience building and deploying ML models in production environments (e.g. REST/gRPC endpoints, containerised models, model serving platforms).
Proficiency in Python and experience with ML/AI frameworks.
Experience with evaluation frameworks, CI/CD pipelines, and monitoring stacks.
Strong systems thinking and performance engineering mindset.
Familiarity with techniques for safe deployment, evaluation metrics, model guardrails, and A/B testing in ML systems.
Join Us
If you’re excited to turn cutting-edge AI into robust, production-ready systems that power the future of education — we’d love to hear from you. Join us to build AI solutions that matter for the next generation of learners, educators, and public service!
GovTech is an equal opportunity employer committed to fostering an inclusive workplace that values diverse voices and perspectives, as we believe it is key to innovation.
Our employee benefits are based on a total rewards approach, offering a holistic and market-competitive suite of perks.
We champion flexible work arrangements (subject to your job role) and trust you to manage your time to deliver your best.
Learn more about life inside GovTech at go.gov.sg/GovTechCareers.
* Salary range is an estimate based on our AI, ML, Data Science Salary Index 💰
Tags: A/B testing CI/CD Computer Science Engineering Generative AI LLMs Machine Learning ML infrastructure ML models Model deployment Model inference Pipelines Python Testing
Perks/benefits: Career development Flex hours
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