EAIG:Head – Responsible AI

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.
Key Responsibilities:
. 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)
. 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
. Design or enforce a model inventory and tiering system.
. Conduct or oversee independent model validation
. Define escalation protocols for model incidents and breaches
. Familiar with SR 11-7 / SS1/23 (international MRM standards) and their adaptation to India
. Develop or implement an algorithmic fairness testing protocol.
. Define human-in-the-loop requirements for high-stakes AI decisions (credit, fraud, KYC)
. Led ethical review of customer-facing AI (chatbots, recommendation engines)
. Produce customer-facing explainability communications
. Embed DPDP consent requirements into AI data pipelines
. Conduct Data Protection Impact Assessments (DPIAs) for AI projects.
. Coordinates with the DPO and CISO on data minimization for model training
. Monitors MeitY notifications and Supreme Fiduciary obligations
. Built an AI Ethics Council or Responsible AI Committee
. Authored Board-level AI risk appetite and policy documents
. Integrate AI governance into the bank's Defense model
. Establish an AI incident register and root-cause analysis process
. Present AI risk dashboards to Board Risk or Audit Committees
. Manage RBI inspection interactions on model risk topics
. Publish thought leadership (papers, conference talks, regulatory submissions)
. Runs cross-functional working groups spanning Technology, Compliance, Legal, and Business
. Built and led teams of 8+ spanning AI ethics, model risk, and policy analysts.
. Define the RA function's OKRs and measured maturity against international benchmarks.
. Create training and awareness programs for data scientists and product managers.
. Establish communities of practice for responsible AI across business units
Role Proficiency/Ideal Experience Profile:
. 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 - Strong 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.