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

Maybank; Kuala Lumpur; On-site; MYR 180,000 - 300,000; Full time

Job summary

Maybank is seeking a Data Engineer to build, deploy and support AI use cases for internal audit. The role involves developing robust data pipelines, APIs and extensions, while operating the audit AI lab and maintaining a central prompt library.

You will work to ensure responsible AI, governance and secure handling of confidential data, with production-ready pipelines that run reliably and unattended.

Qualifications

• Degree in Computer Science, Software or Data Engineering, Data Science, Information Technology or a related field.
• 1–5 years’ experience building and deploying data or AI solutions, ideally within financial services.
• 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.
• 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).
• A sound understanding of responsible-AI principles, AI governance and the secure handling of confidential data.

Responsibilities

• Build, deploy and support approved AI use cases, agents and reusable assets for internal audit.
• Develop tools, applications and extensions for audit use cases with pro-code and low-code options.
• Operate and maintain the audit AI lab, prompt library and use-case catalogue.
• Design, build and optimise scalable data pipelines linking source systems to a governed analytics environment.
• Establish read-only, least-privilege data access and maintain API integrations to audit data sources.
• Schedule, orchestrate and monitor continuous-assurance pipelines to run reliably in production.

Skills

Data pipelines

ETL/ELT

API integration

Production experience

LLM integration

Vector databases

RAG techniques

Microsoft AI stack

Responsible AI

Education

Degree in Computer Science, Software or Data Engineering, Data Science, IT or related field

Job description

• 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.