Data and AI Governance Lead

RADIANT DIGITAL SOLUTIONS PTE. LTD.; Singapore; On-site; SGD 90,000 - 120,000; Full time

Job summary

A prominent tech solutions company in Singapore is seeking a Data & AI Governance Lead to implement governance frameworks for AI initiatives. The ideal candidate will have a strong background in data governance within regulated industries, expertise in tools like Azure and Databricks, and a solid understanding of regulatory needs. Responsibilities include defining data access models and ensuring compliance with data usage standards across various platforms. This role emphasizes stakeholder collaboration to enhance governance alignment and innovation.

Qualifications

• Experience in data governance initiatives in regulated industries.
• Hands-on with Azure, Databricks, and governance tools.
• Strong skills in policy authoring and implementation.

Responsibilities

• Design and implement data and model governance for AI.
• Define data classification and access models.
• Ensure compliance with local and global regulations.

Skills

Data governance leadership

Stakeholder management

Understanding of regulatory expectations

Tools

Azure

Databricks

Collibra

MLflow

Job description

Overview

We are seeking a Data & AI Governance Lead to design, implement, and operationalize enterprise‑grade data and model governance for AI initiatives in a regulated environment. This role will ensure that AI, ML, and GenAI solutions are secure, compliant, auditable, and scalable, while enabling innovation across business teams.

The role will work closely with Data Management Office (DMO), Risk, Compliance, Finance, IT, and business stakeholders, translating regulatory expectations into practical, enforceable controls across Azure, Databricks, and AI platforms.

Key Responsibilities

• Data Governance for AI
  • Define and implement enterprise‑wide data classification and attribute tagging to support AI usage, including:
    • PII / PCI / PHI
    • Sensitivity tiers
    • Usage restrictions and consent
  • Design access and entitlement models for AI and agent‑based use cases, covering:
    • Azure Entra ID integration
    • RBAC / ABAC
    • Privileged access management
    • Break‑glass procedures
    • End‑to‑end audit trails
  • Establish the metadata, catalog, and lineage operating model using tools such as Collibra and/or Microsoft Purview, including:
    • Lineage and traceability for AI pipelines
    • Policies for API‑based data acquisition
    • Governance of vector databases and RAG stores
  • Define and document cross‑border data usage rules, aligned with global and local regulations (e.g., MAS, HKMA, JFSA, GDPR, PDPA).
  • Set governance boundaries and guidance for Databricks and Azure platforms, including:
    • Unity Catalog governance
    • Lakehouse permissions
    • Delta Sharing
    • Clear “what data can be stored where” principles
• Model Governance (LLM / ML)
  • Establish bank‑grade model lifecycle governance, including:
    • Central model inventory
    • Risk classification and tiering
    • Approval workflows and sign‑offs
    • Model documentation and validation standards
    • Human‑in‑the‑loop controls
  • Implement monitoring and observability for ML and LLM models, covering:
    • Model drift, bias, and performance
    • Prompt and output logging
    • Toxicity detection and content filtering
    • Rollback mechanisms and change control
    • Integration with MLflow / Model Registry
  • Define and operationalize AI safety processes, including:
    • Red teaming and adversarial testing
    • Prompt hygiene and secure prompt design
    • Sensitive data minimization and retention controls
    • AI incident response and escalation procedures
• Ways of Working & Operating Model
  • Define the AI governance framework, including:
    • Standards, policies, and operating procedures
    • Clear RACI across Data, Risk, IT, and business teams
  • Partner with DMO, Risk, Compliance, Finance, and IT to ensure alignment with enterprise governance and regulatory expectations.
  • Translate governance principles into practical, implementable controls for:
    • Azure cloud services
    • Databricks Lakehouse
    • LLM platforms and services
    • AI agents used by business teams
  • Act as a trusted advisor to delivery teams, balancing risk management with innovation enablement.

Candidate Profile (Must Have)

• Proven experience leading data governance and model governance initiatives in regulated industries, preferably financial services.
• Hands‑on experience with:
  • Azure (including Entra ID)
  • Databricks and Unity Catalog
  • MLflow / Model Registry
  • Collibra and/or Microsoft Purview
  • Enterprise IAM and access control models
• Strong understanding of regulatory expectations for data usage and model risk management, particularly within APAC jurisdictions.
• Demonstrated ability to author policies, standards, and operating procedures, and guide their implementation with both IT and business stakeholders.
• Excellent stakeholder management skills, with the ability to influence across senior technical and non‑technical audiences.