Job Responsibilities:

BUILD AND DEPLOY AI SOLUTIONS

Build, deploy and support approved AI use cases, agents and reusable assets for internal audit.
Develop tools, applications and extensions for audit use cases where custom (pro-code) development is required, balancing these with low-code and configurable solutions to keep tools maintainable and supportable.
Operate and maintain the audit AI lab, prompt library and use-case catalogue.

DATA ENGINEERING FOR CONTINUOUS ASSURANCE

Design, build and optimise scalable, maintainable data pipelines that connect approved source systems — databases, data warehouses and data lakes to a governed analytics environment supporting AI-led continuous assurance and monitoring.
Establish read-only, least-privilege data access in line with data-owner, IT Security, second-line and CISO requirements, reflecting the sensitivity of audit data.
Build and maintain API integrations and connectors (e.g. MCP) to approved audit data sources.
Schedule, orchestrate and monitor continuous-assurance pipelines and agents so they run reliably and unattended in production, with alerting on failures and on control drift.

RESPONSIBLE AI AND QUALITY

Apply responsible-AI guardrails, risk controls, explainability, human-in-the-loop checkpoints and audit-trail logging to every solution, so that AI outputs are transparent, reviewable and owned by the auditor.
Support the progression of use cases from proof-of-concept to controlled deployment, with appropriate quality assurance, validation, testing and documentation.
Maintain data governance, quality, confidentiality and regulatory compliance across the AI and analytics environment, consistent with Group policy and applicable technology and AI expectations (e.g. BNM RMiT and MAS requirements).

ENABLEMENT AND COLLABORATION

Work closely with auditors to translate audit requirements into scalable, maintainable solutions, and build auditors capability and confidence in using approved AI tools.
Contribute to the unit's library of reusable AI assets and its central knowledge portal.
Continuously optimise the performance, reliability and scalability of deployed AI and data solutions.

Job Requirements:

Degree in Computer Science, Software or Data Engineering, Data Science, Information Technology or a related field.
Around 1-5 years experience building and deploying data and / or AI solutions, ideally within financial services or another regulated environment.
Strong, hands-on software development and data-engineering skills, including data pipeline (ETL / ELT) implementation, data warehousing and API integration.
Practical experience operating data or AI pipelines in production — scheduling, orchestration, monitoring and alerting — so continuous processes run reliably and unattended.
Practical, demonstrable experience with the Microsoft AI and data stack.
Working knowledge of large language model (LLM) integration and retrieval techniques (e.g. vector databases / RAG) for AI-powered search and analytics.
A sound understanding of responsible-AI principles, AI governance and the secure handling of confidential data.

AI Specialist, AI & Data Engineering, AI Advisory, Group Audit