About the Role

We are seeking highly experienced Senior AI Engineers to lead the design, implementation, and scaling of  AI infrastructure. In this role, you will bridge the gap between data science and software engineering by building robust, automated pipelines and establishing best practices for model development, deployment and lifecycle management.

We are looking for someone who is passionate about building highly efficient, reusable, and developer-friendly AI systems.

Why Join Us

At the Agency for Science, Technology and Research (A*STAR), Singapore’s leading public sector R&D agency, you will work at the vibrant intersection of frontier scientific research and real-world industrial translation. Engineers at A*STAR have the unique opportunity to design and build AI infrastructure that scales across incredibly diverse, multi-disciplinary domains—from advanced manufacturing and digital healthcare to sustainability and transportation.

A*STAR heavily invests in its engineers’ growth, offering a highly collaborative research culture, competitive benefits, robust pathways for continuous learning, and the unique chance to work on nationally-significant Smart Nation initiatives that impact lives globally.

What You Will Do

• Drive Data Leadership: Provide authoritative technical leadership on critical data infrastructure, championing privacy-first architectures, complex transformations, and risk-aware AI training approaches.
• Architect the Engine: Design, build, and relentlessly optimize a robust, next-generation data tech stack and ETL/ELT pipelines engineered for massive scalability and high-velocity efficiency.
• Guarantee Integrity: Establish and enforce uncompromising validation frameworks to ensure absolute data accuracy, consistency, and integrity—eliminating "garbage in, garbage out" for our AI systems.
• Align with Strategy: Strategically align the core data infrastructure with SDSO objectives and critical project needs, ensuring seamless support for advanced analytics and enterprise-scale transformations.
• Shape the Narrative: Bridge the gap between deep technical implementation and strategic vision. You will ensure complex data handling constraints and assumptions are explicitly and compellingly reflected in the technical narrative of project proposals.

Requirements

• Proven Scale: 1 to 6+ years of experience in Software Engineering, Platform Engineering, or DevOps, with at least 3+ years strictly dedicated to MLOps, AI Infrastructure, or ML Engineering in a high-traffic, production environment. Candidates with no experience but with great interest in mastering advanced AI capabilities are welcome to apply. 
• Architectural Vision: Demonstrated experience designing, building, and maintaining end-to-end ML platforms from the ground up, moving beyond single-model deployments to centralized organizational infrastructure.
• Engineering Rigor: Deep proficiency in Python and strong knowledge of at least one high-performance backend language (e.g., Go, C++, Rust, or Java). You write clean, testable, and production-grade code.
• Cloud & Container Mastery: Experience with cloud-native architectures (AWS, GCP, or Azure) and deep fluency in containerization and orchestration (Docker, Kubernetes, Helm).