Data Governance and AI Governance Architect / Lead

Purpose: Lead the design and implementation of the organization's Data Governance and AI governance framework, operating model, and governance roadmap to improve data control, accountability, compliance, and business value.

Key Responsibilities

- Define and implement the Data Governance framework, charter, operating model, and stakeholder RACI.
- Establish governance standards for metadata, business glossary, stewardship, classification, access, Data Quality and policy compliance.
- Align governance practices with regulatory and privacy requirements, including DPDP readiness.
- Lead DG tool assessment, selection support, onboarding, and implementation oversight.
- Drive onboarding of data owners and data stewards across business and technology teams.
- Define governance KPIs, control metrics, audit mechanisms, and continuous improvement plans.
- Conduct governance assessments and translate findings into phased implementation roadmaps.
- Run governance committees, stakeholder reviews, awareness sessions, and change management activities.

Define and implement enterprise AI Governance policies , standards, controls, and operating procedures aligned with Responsible AI principles.

- Establish governance practices for AI/ML models, Generative AI applications, training data, prompts, model outputs, and AI lifecycle management.
- Develop AI risk management frameworks covering bias, fairness, explainability, accountability, transparency, privacy, security, and ethical AI usage.
- Collaborate with Data Science, Technology, Security, Legal, Compliance, and Risk teams to establish AI governance operating models and control frameworks.
- Define standards for AI model inventory, model documentation, approval workflows, monitoring, validation, and periodic review processes.
- Establish governance controls for AI training datasets, synthetic data usage, data lineage,



data provenance, and model traceability.

Required Skills:

• Strong knowledge of enterprise D ata Governance framewo rks, operating models, and controls.

- Experience with metadata management, business glossary, lineage, stewardship, and policy management.
- Understanding of data privacy, compliance, and access governance concepts.
- Solid stakeholder management, workshop facilitation, and documentation skills.
- Ability to define governance metrics, implementation plans, and target operating models.

Strong understanding of AI Governance, Responsible AI, AI Risk Management, and AI compliance frameworks.
- Knowledge of AI/ML lifecycle management, Generative AI concepts, Large Language Models (LLMs), and model governance practices.
- Experience defining governance controls for AI models, training data, model monitoring, explainability, and validation processes.
- Understanding of ethical AI principles including fairness, accountability, transparency, bias mitigation, and human oversight.
- Familiarity with AI governance standards and frameworks such as NIST AI Risk Management Framework (AI RMF), ISO/IEC 42001, OECD AI Principles, and emerging AI regulations.
- Understanding of AI security, privacy, model risk, prompt governance, and AI-related compliance requirements.

Preferred Experience: 8-12 Years

- Experience leading enterprise DG programs across multiple business domains.
- Exposure to DG and DQ platforms such as Collibra, securiti.ai , Microsoft Purview, Alation, Informatica, or similar tools.

Experience establishing or supporting AI Governance, Responsible AI, or Model Risk Management initiatives.
- Experience governing AI/ML, Advanced Analytics, or Generative AI solutions in enterprise environments.
- Experience working with legal, risk, security, and business leadership teams.
- Familiarity with modern cloud and lakehouse environments.

Familiarity with data privacy regulations like GDPR, DPDPA, CCPA, HIPAA etc

📌 AI and Data Governance Manager (India)
🏢 Ekloud
📍 India