Job Summary

About the Role

We are seeking highly experienced 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

• Architect the Future: Translate high-level business goals into robust, scalable, and autonomous multi-step agentic workflows that deliver real-world value.
• Own the Stack: Drive the development of core agentic components. You will define the tech stack and integrate state-of-the-art tools tailored for diverse, high-impact use cases.
• Design Collaboration: Craft advanced multi-agent communication frameworks, memory management systems, context engineering pipelines, and human-in-the-loop feedback mechanisms to ensure seamless, deterministic behavior.
• Pioneer AI Safety: Partner with our AI Safety team to build highly secure, private, and robust systems. You will lead the charge in mitigating semantic drift, tracking confidence levels, and aligning agents with long-horizon tasks.
• Set the Standard: Implement rigorous evaluation and benchmarking protocols to objectively measure, refine, and prove the effectiveness of our autonomous systems.

Requirements

• Proven Scale: 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. Fresh graduates 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).