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.

Qualified Certifications (Preferred)

- Microsoft Azure AI Engineer
- AWS Machine Learning Specialty
- Google Professional Machine Learning Engineer
- Databricks Machine Learning Professional
- TOGAF or Enterprise Architecture Certification
- Certified Scrum Product Owner / SAFe
- AI Governance or Responsible AI certifications

Experience

- 15–20 years of overall IT experience.
- Minimum 8 years in AI, Machine Learning, or Advanced Analytics leadership roles.
- Proven experience leading enterprise AI transformations.
- Experience building and scaling AI engineering teams.
- Demonstrated success in delivering AI solutions in production.
- Experience in BFSI, NBFC, Banking, Insurance, or Fin Tech is highly preferred.

Experience working with senior leadership and CXOs

📌 AVP - AI & Intelligent Automation (India)
🏢 Antal International
📍 India