Asks for

The ad says“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).”
The ad says“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).”
The ad says“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.”
The ad says“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).”
The ad says“Working knowledge of data access control and data orchestration.”
The ad says“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).”
The ad says“Working knowledge of data access control and data orchestration.”
The ad says“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).”
The ad says“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).”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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).”
The ad says“Solid scripting/programming skills (e.g. Python, SQL) and comfortable reading other people's code across the stack.”
The ad says“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).”
The ad says“Solid scripting/programming skills (e.g. Python, SQL) and comfortable reading other people's code across the stack.”
The ad says“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.”
The ad says“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).”

AI Singapore (AISG) is a national AI programme launched by the National Research Foundation (NRF), Singapore, to build and anchor deep national capabilities in AI. AISG is supported through a government-wide partnership including the NRF, Ministry of Digital Development and Information (MDDI), Infocomm Media Development Authority (IMDA), Economic Development Board (EDB) and Enterprise Singapore (ESG). We bring together research institutions and the vibrant ecosystem of AI start-ups and companies to support impactful research, develop talent, and power Singapore's AI efforts.

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

We're looking for an AI Engineer to join the Platform team within AI Products at AISG. In this role, you will design, build, and scale the data infrastructure that powers large language model (LLM) training, fine-tuning, evaluation, and retrieval-augmented generation (RAG) across the organisation. Your work will directly contribute to the architecture and reliability of data storage systems, data pipelines, and the compute infrastructure that supports large-scale AI workloads.

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.

Good to Have:

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

Asks for

Values