GRIT Trainee, Data AI Engineer
Job Overview
Get to Know the Team
The trainee will join the Metadata & BI Platform Team within the Data Engineering Platforms department. The team manages the data discovery platform (Hubble), data observability platform (Genchi), the BI Platforms (Superset & Power BI) as well as the Data Agent in GrabGPT. The team comprises of 10 engineers based in SG and MY.
Get to Know the Role
This is a 6 months full-time onsite role based in Singapore as part of the GRIT Programme
This traineeship offers fresh graduates an exciting opportunity to delve into the dynamic field of data engineering and artificial intelligence (AI). Trainees will work in a collaborative and innovative environment, gaining hands-on experience in designing, implementing, and managing AI-driven data platforms that power Grab's data-driven operations.
The program provides exposure to real-world challenges in big data engineering and AI integration, working alongside a world-class engineering team managing petabytes of data across Southeast Asia. Trainees will have the opportunity to learn cutting-edge tools and technologies, fostering continuous innovation and accelerating their growth in technical expertise.
The Critical Tasks You Will Perform
During the traineeship, trainees will play an active role in building and optimizing AI-driven data platforms. They will gain hands-on experience in the following areas:
- Designing and implementing RESTful APIs and efficient backend systems to support data platforms.
- Identifying, debugging, and resolving issues within big data platforms to ensure data quality and integrity.
- Writing unit, functional, and end-to-end tests to ensure the reliability and robustness of systems.
- Incorporating LLM APIs into platform workflows for intelligent automation and enhanced user experiences.
- Building and orchestrating agentic systems using frameworks like LangGraph for applications such as data issue troubleshooting, code generation, and business question answering.
- Designing and optimizing vector search and Retrieval-Augmented Generation (RAG) pipelines to support embedding-based retrieval and advanced data insights.