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Company Overview
The lead government agency in Singapore drives the nation’s Smart Nation initiative and public sector digital transformation.
Lead AI Engineer (AI Capability Development & Infrastructure), DXD
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 digital platforms within the education sector.
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.
Disclaimer
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