AI Singapore (AISG) is a national AI programme launched by the National Research Foundation (NRF) to anchor deep national capabilities in Artificial Intelligence (AI). Hosted by the Nanyang Technological University (NTU), AISG brings together all Singapore-based research institutions and the vibrant ecosystem of AI start-ups and companies developing AI products to perform use-inspired research, grow the knowledge, create the tools, and develop the talent to power Singapore's AI efforts.

We're looking for an AI Engineer, Full Stack to join the Data Platform team within AI Products at AISG. ATLAS Data Platform is a data platform which supports a global effort to collect multimodal language and cultural data. You'll develop modules end-to-end for this platform from UI/UX through to backend services and deploy them on cloud infrastructure. You'll work heavily with AI tools in your day-to-day engineering workflow, and you'll build features that support evaluations, benchmarks, and data annotation across our internal teams and external partner communities. This role calls for solid engineering fundamentals, comfort working across the stack, and an appetite for shipping.

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.

Duties & Responsibilities:

Platform development and engineering

• Design, build, and maintain modules of the data platform across the full stack such as frontend interfaces, backend services, APIs, and data models to support multimodal data collection, curation, and access.

• Use AI tools (e.g. Claude, Copilot, Cursor) deliberately in your engineering workflow for code generation, refactoring, code review, debugging, and documentation and help the team codify good practices around AI-assisted development.

• Write clean, well-tested, maintainable code; participate in code reviews and design discussions; and contribute to the platform's technical documentation.

Domain workstreams: evaluations, benchmarks, and annotation

• Build features and tooling that support model and data evaluations, benchmark execution, and result tracking across the platform.

• Develop interfaces and workflows for data annotation used by internal teams and external community partners including task design, quality control, reviewer workflows, and progress tracking.

• Integrate with relevant evaluation frameworks, benchmark suites, and annotation tools, and help the team make sound build-vs-adopt decisions.

Infrastructure and deployment

• Deploy and maintain platform UI/UX and backend services on Google Cloud Platform (GCP) and Amazon Web Services (AWS), including configuration, networking, and storage.

• Set up and maintain CI/CD pipelines, monitoring, logging, and alerting to ensure reliability, performance, and security across environments.

• Manage cloud resources cost-effectively, and contribute to architecture decisions on scalability, multi-region considerations, and data residency.

Collaboration and delivery

• Work closely with the Project Manager, data engineers, researchers, and external partners to translate requirements into shippable features at a steady cadence.

• Participate in incident response, post-mortems, and continuous improvement of the platform's operations.

Requirements:

You should be a hands-on full stack engineer who is comfortable across frontend, backend, and cloud, and who actively uses AI tools to move faster and produce higher-quality work.

The ideal candidate would have:

• A degree in Computer Science, Information Technology, or equivalent.

• Around 2-3 years of full stack development experience, shipping production features in a modern web stack (e.g. TypeScript/React on the frontend; Python or Node.js on the backend; REST/GraphQL APIs; SQL and/or NoSQL data stores).

• Hands-on experience deploying applications on GCP and AWS, including containerised workloads, managed services, and CI/CD.

• 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.

• Familiarity with data platform concepts: pipelines, storage, metadata, catalogues, and access controls.

• Working knowledge of AI/ML evaluation, benchmarking, and/or data annotation workflows, and the tooling ecosystem around them (e.g. evaluation harnesses, annotation platforms).

• Strong communication skills and the ability to work with both technical and non-technical stakeholders across cultures and time zones.

• Bonus: experience with multimodal data systems (text, audio, image, video); contributions to open-source AI/data tooling; experience operating within Singapore public sector or research programmes.

We regret that only shortlisted candidates will be notified.