ML/AI Engineer

Requirements

Experience: 3–5 years of hands-on experience building and deploying ML/AI

applications. This must include at least one LLM-enabled system delivered beyond

prototype into operational use.

B. Technical & Professional Competencies

• Strong Python software engineering capability, including API development,

testing, version control and modular system design.

• Hands-on experience building and deploying ML/AI applications, including at

least one LLM-enabled or agentic AI solution beyond proof-of-concept stage.

• Experience with LLM applications, retrieval, tool use, workflow orchestration or

multi-agent frameworks such as LangGraph, CrewAI or equivalent.

• Experience integrating AI/ML solutions with enterprise data platforms, databases,

APIs or cloud services.

• Ability to design and implement systematic evaluation frameworks for AI systems,

including testing, benchmarking, observability and reproducibility.

• Working knowledge of AI governance, security and Responsible AI practices,

including human-in-the-loop controls, access management, auditability and

safeguards against misuse.

Preferred (Advantageous)

• Experience with AWS services such as Bedrock, Redshift, Glue, S3, Lambda or

related cloud-native services.

• Experience with PySpark, Databricks or large-scale data processing and feature

engineering pipelines.

• Experience with CI/CD, containerisation, MLOps or model deployment tooling

(e.g. Docker, MLflow, GitHub Actions, Jenkins).

Other

Work location- Paya Lebar The ML/AI Engineer is embedded within Data Analytics and AI Division (DSAD), working as part of the division's Fraud Analytics Model (FAM) team.