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AI Engineer, Data Infra

Ntu

This position will be hosted at the Nanyang Technological University (NTU) under VP (Artificial Intelligence & Digital Economy)’s office and we welcome you to join our community.

About Ntu

AI Singapore (AISG) is a national initiative driven by the Ministry of Digital Development and Information (MDDI), the Infocomm Media Development Authority (IMDA), and the National Research Foundation, Singapore (NRF).

AISG brings together Singapore-based research institutions and the broader ecosystem of AI start-ups and companies to support impactful research, develop talent and power Singapore's AI efforts.

For more information on AI Singapore, please visit https://www.aisingapore.org .

Responsibilities

  • Data infrastructure and architecture Develop and maintain the overall data architecture, ensuring it scales to support AI training, fine-tuning, evaluation, and RAG workloads.
  • Define and manage the data technology stack, evaluating and adopting tools that best fit evolving data needs.
  • Build automation for data transfer and backup, and support data cataloging/discovery tools.
  • Design and maintain data pipelines (batch and streaming) using tools like AWS Glue and Amazon EMR.
  • Data governance and management Architect and govern org-level IAM policy across AWS Organizations including SCPs, cross-account roles, and permission boundaries to enforce least-privilege access at scale.
  • Review and enhance data access patterns for performance and cost optimization.
  • Develop and manage data analytics and dashboarding capabilities using tools like Amazon Athena and QuickSight to give stakeholders visibility into platform usage and cost.
  • AI-assisted ops and continuous improvement Use AI tools (e.g. Claude, Copilot, Cursor) appropriately in your daily work responsibilities.
  • Build internal tools leveraging AI to reduce manual effort in day-to-day operations.

Requirements

  • You should be a hands-on engineer who enjoys both building robust infrastructure and designing tools that other engineers and researchers want to use.
  • You should be comfortable balancing platform ownership (architecture, cost, governance) with product thinking (usability, self-service, adoption).
  • A 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 operating production systems at scale.
  • Strong knowledge of distributed data systems and storage technologies (object storage, data lakes, distributed file systems, vector databases) and data pipelining tools (e.g. Apache Spark, Apache Airflow, Ray, Dagster).
  • Working knowledge of data access control and data orchestration.
  • Familiarity with cloud organisational structures (e.g. AWS Organizations, GCP folder/project hierarchy), including multi-account/multi-project setups, org-level IAM, and billing/cost allocation.
  • Hands-on experience operating workloads on different cloud providers including IaC (e.g. Terraform), containers and orchestration (e.g. Docker, Kubernetes), and managed services for compute, storage, and networking.
  • Demonstrated use of AI tools (e.g. Claude, Copilot, Cursor) in your day-to-day engineering — for code generation, review, debugging, and documentation — with a clear sense of where they help and where they don't.
  • Solid scripting/programming skills (e.g. Python, SQL) and comfortable reading other people's code across the stack.
  • Strong communication skills and a team collaborator.

Preferred

  • Experience with LLM training dataset (e.g. Common Crawl).
  • C/C++/Rust/Go or other relevant programming languages.
  • Contributions to open-source AI/ML projects.
  • We regret that only shortlisted candidates will be notified.

Hiring Institution

NTU

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