Role description

About the

Role

India's banking sector is undergoing rapid AI adoption — from credit scoring and fraud detection to customer service automation and liquidity management. In August 2025, the Reserve Bank of India published the FREE-AI (Framework for Responsible and Ethical Enablement of Artificial Intelligence) report, establishing seven guiding 'Sutras' and 26 recommendations across six strategic pillars for all regulated entities. Simultaneously, the Ministry of Electronics and IT (MeitY) released national AI Governance Guidelines in November 2025 under the IndiaAI Mission.

Large private sector banks — already at the frontier of AI deployment — now face a dual mandate: accelerating AI-driven innovation while demonstrating board-level accountability for model risk, algorithmic fairness, explainability, and DPDP Act 2023 compliance. The Head of Responsible AI will be the internal authority who translates regulatory expectation into operational governance, and who builds trust with the RBI, customers, and the Board.

Competency Framework

1. Regulatory & Policy Fluency

Deep understanding of the RBI FREE-AI framework, DPDP Act 2023, SEBI AI guidelines, and MeitY AI Governance Guidelines. Ability to anticipate regulatory evolution and position the bank ahead of compliance deadlines.

- Can map all 26 FREE-AI recommendations to existing bank processes
- Leads RBI inspection readiness for AI/ML models
- Tracks SEBI, IRDAI, and CERT-In AI-related advisories proactively
- Engages with regulator-industry working groups (e.g., RBI Innovation Hub)

2. AI/ML Technical Grounding Sufficient technical depth to evaluate model architecture, training data quality, fairness metrics, and explainability methods — without necessarily being a hands-on data scientist.

- Evaluates XAI outputs (SHAP, LIME) and can explain them to non-technical audiences
- Identifies bias in training data (demographic, historical, proxy)
- Reviews model cards and datasheets for completeness
- Understands drift, hallucination risk, and uncertainty quantification

3. Model Risk Governance Experience establishing or operating a Model Risk Management (MRM) framework covering approval, validation, deployment, monitoring, and retirement of AI/ML models across the bank.

- Designed or enforced a model inventory and tiering system




- Conducted or overseen independent model validation
- Defined escalation protocols for model incidents and breaches
- Familiar with SR 11-7 / SS1/23 (international MRM standards) and their adaptation to India

4. Ethics & Fairness Ability to operationalise AI ethics principles — fairness, transparency, human oversight, non-discrimination — within commercial constraints, anchored to the FREE-AI Sutras and the bank's own values.

- Developed or implemented an algorithmic fairness testing protocol
- Defined human-in-the-loop requirements for high-stakes AI decisions (credit, fraud, KYC)
- Led ethical review of customer-facing AI (chatbots, recommendation engines)
- Produced customer-facing explainability communications

5. Data Privacy & Security Working mastery of India's Digital Personal Data Protection Act 2023, including data principal rights, consent frameworks, cross-border transfer restrictions, and obligations of data fiduciaries.

- Embedded DPDP consent requirements into AI data pipelines
- Conducted Data Protection Impact Assessments (DPIAs) for AI projects
- Coordinates with the DPO and CISO on data minimisation for model training
- Monitors MeitY notifications and Supreme Fidiuciary obligations

6. Governance Architecture Ability to design and operate a cross-functional Responsible AI governance structure — including policies, committees, risk appetite statements, and board reporting cadences.

- Built an AI Ethics Council or Responsible AI Committee
- Authored Board-level AI risk appetite and policy documents
- Integrated AI governance into the bank's Three Lines of Defence model
- Established an AI incident register and root-cause analysis process

7. Stakeholder Management & Communication Able to translate complex AI risk topics into language accessible to the Board, RBI examiners, external auditors, and retail customers. Builds credibility across technology, risk, legal, and business lines.





- Presented AI risk dashboards to Board Risk or Audit Committees
- Managed RBI inspection interactions on model risk topics
- Published thought leadership (papers, conference talks, regulatory submissions)
- Runs cross-functional working groups spanning Technology, Compliance, Legal, and Business

8. Leadership & Team Building Experience building a responsible AI function from near-zero — hiring specialists, managing budget, and embedding the function's mandate across product and technology teams.

- Built and led teams of 8+ spanning AI ethics, model risk, and policy analysts
- Defined the RA function's OKRs and measured maturity against international benchmarks
- Created training and awareness programmes for data scientists and product managers
- Established communities of practice for responsible AI across business units

Role Proficiency/Ideal Experience Profile:

Background

What makes it relevant

Model Risk / Quant Risk in banking

Deep understanding of validation, SR 11-7 style frameworks, and regulatory engagement on models. Easiest to retool for the AI-specific dimension.

Applied AI / Data Science leadership

Technical credibility with data science teams; understands ML pipelines, model drift, and deployment risk. Needs to build out the governance and policy muscle.

Technology law / regulatory advisory

Solid regulatory navigation and DPDP Act fluency; experienced in engaging with MeitY and RBI on policy. Needs a technical co-lead for model risk depth.

AI governance / policy (Big Tech or consulting)

Experience designing AI ethics frameworks, conducting impact assessments, and stakeholder engagement at scale. Banking domain knowledge needed as add-on.

Digital / fintech transformation

Understands how AI is operationally deployed in banking; credible with business and technology teams. Governance and risk framework skills need development.

Qualifications:

Post-graduate degree (MBA, MTech, LLM, or equivalent) from a top-tier institution. 12–18 years of total experience, with at least 4–6 years in an AI/data-adjacent leadership role. Familiarity with Indian financial regulation is non-negotiable; international experience in markets with mature AI regulation (EU AI Act, UK FCA) is a strong plus.

📌 EAIG:Head – Responsible AI (Mumbai)
🏢 Axis Bank
📍 Mumbai