AVP - AI & Intelligent Automation

Key Responsibilities: Enterprise AI Strategy: Develop and execute the enterprise AI strategy aligned with business and digital transformation goals. Build a multi-year AI roadmap across all business functions. Identify high-value AI opportunities that improve customer experience, operational efficiency, and revenue growth. Drive AI adoption across lending, customer service, collections, sales, risk, finance, HR, legal, and operations. Establish AI as a core capability within the organization. Build & Lead the AI Centre of Excellence: Establish the Enterprise AI & Intelligent Automation CoE. Recruit, mentor, and lead a multidisciplinary team comprising AI architects, AI engineers, ML engineers, data scientists, MLOps engineers, AI platform engineers, and AI governance specialists. Foster a culture of innovation, experimentation, collaboration, and continuous learning. Define operating models, delivery standards, and engineering best practices AI Product & Solution Delivery: Own the end-to-end delivery of enterprise AI initiatives including: AI-powered Customer Service Enterprise AI Assistants Employee Copilots AI-enabled CRM AI-powered Loan Origination Intelligent Underwriting Fraud Detection Collections Intelligence Risk Analytics Document Intelligence Marketing Personalization Conversational Analytics Executive AI Dashboards Agentic AI Workflows Intelligent Decision Engines Enterprise Knowledge Management Ensure solutions move successfully from concept to production with measurable business outcome AI Engineering & Architecture: Define enterprise AI architecture and reference frameworks. Standardize reusable AI services and components. Build scalable AI platforms capable of supporting enterprise-wide adoption. Define AI integration standards with enterprise applications including CRM, Mobile App, LOS, LMS, Data Lake, APIs, Contact Centre, Marketing Platforms, and Digital Portals. Drive cloud-native AI architecture and API-first development Generative AI & Agentic AI: Lead implementation of: Large Language Models (LLMs) Retrieval-Augmented Generation (RAG) Multi-Agent Systems AI Agents AI Orchestration Prompt Engineering Frameworks Enterprise Knowledge Assistants AI Copilots Intelligent Workflow Automation Conversational AI AI Search Evaluate emerging AI technologies and identify opportunities for enterprise adoption. Data Science & Machine Learning: Lead the development of: Credit Risk Models Customer Segmentation Cross-sell and Upsell Models Churn Prediction Collections Optimization Fraud Detection Models Recommendation Engines Forecasting Models NLP Solutions Customer Lifetime Value Models Ensure models are scalable, explainable, and business-ready. AI Platform & MLOps Establish enterprise AI infrastructure and deployment pipelines. Define model lifecycle management processes. Build CI/CD pipelines for AI applications. Establish model monitoring, observability, and drift detection. Define standards for feature stores, vector databases, and model registries. Optimize AI platform performance, reliability, and cost AI Governance & Responsible AI: Define enterprise AI governance policies and standards. Ensure compliance with RBI regulations, internal risk policies, and applicable data privacy requirements. Establish controls for model validation, explainability, fairness, bias detection, and auditability. Lead AI risk assessments and governance forums. Promote ethical and responsible AI practices across the organization Stakeholder & Vendor Management Partner with CXOs, Business Heads, Risk, Compliance, Operations, IT, and Product teams. Build executive-level relationships to identify and prioritize AI opportunities. Manage AI technology vendors, cloud partners, and consulting organizations. Evaluate AI platforms, tools, and partnerships to maximize business value. Innovation & Emerging Technologies: Continuously evaluate and drive adoption of: Generative AI Agentic AI Autonomous Decision Systems Intelligent Process Automation AI-powered Analytics Knowledge Graphs Computer Vision Speech AI Document AI AI-powered Customer Engagement Build an innovation pipeline and incubate AI use cases through proofs of concept and pilots Financial & Delivery Management Own AI budgets, resource planning, and investment prioritization. Establish AI delivery governance and execution metrics. Measure ROI, productivity gains, operational efficiencies, and customer impact. Ensure timely and high-quality delivery of AI initiatives Educational Qualifications Mandatory Bachelor's degree in Computer Science, Information Technology, Engineering, Data Science, Artificial Intelligence, or a related discipline. Preferred Master's degree in Artificial Intelligence, Data Science, Computer Science, or MBA from a reputed institution. Professional Certifications (Preferred) Microsoft Azure AI Engineer AWS Machine Learning Specialty Google Professional Machine Learning Engineer .