AI Engineer, Data Infrastructure

About the role

Nanyang Technological University (NTU) is hiring an AI Engineer, Data Infrastructure for its AI Singapore (AISG) project, hosted under the VP (Artificial Intelligence & Digital Economy) office. The role builds and maintains the data architecture and platforms that support AI training, fine-tuning, evaluation and RAG workloads.

Key responsibilities

• Develop and maintain the overall data architecture so it scales to support AI training, fine-tuning, evaluation and RAG workloads.
• Define and manage the data technology stack, evaluating and adopting tools that fit evolving data needs.
• Build automation for data transfer and backup, and support data cataloging and discovery tools.
• Design and maintain batch and streaming data pipelines using tools such as AWS Glue and Amazon EMR.
• Architect and govern organisation-level IAM policy across AWS Organizations, including SCPs, cross-account roles and permission boundaries, enforcing least-privilege access at scale.
• Review and improve data access patterns for performance and cost.
• Develop data analytics and dashboarding capabilities with tools such as Amazon Athena and QuickSight to show stakeholders platform usage and cost.
• Use AI tools (e.g. Claude, Copilot, Cursor) in daily work and build internal tools that reduce manual effort.

Required profile

• Degree in Computer Science, Information Technology or equivalent.
• At least 2-4 years of data infrastructure, platform or systems engineering experience, with a track record of running production systems at scale.
• Hands-on engineer who balances platform ownership (architecture, cost, governance) with product thinking (usability, self-service, adoption).
• Demonstrated use of AI tools in day-to-day engineering, with a clear sense of where they help and where they do not.
• Strong communication skills and a team collaborator.

Required skills

• Strong knowledge of distributed data systems and storage (object storage, data lakes, distributed file systems, vector databases) and pipelining tools such as Apache Spark, Apache Airflow, Ray and Dagster.
• Working knowledge of data access control and data orchestration.
• Familiarity with cloud organisational structures (AWS Organizations, GCP folders/projects), multi-account/multi-project setups, org-level IAM and cost allocation.
• Hands-on experience with IaC (e.g. Terraform), containers and orchestration (e.g. Docker, Kubernetes) and managed compute, storage and networking services.
• Solid scripting skills (e.g. Python, SQL) and ability to read other people's code.

Good to have: experience with LLM training datasets (e.g. Common Crawl), C++/Rust/Go, and contributions to open-source AI/ML projects. Only shortlisted candidates will be notified.