AI Product Manager

IndusInd Bank; Bengaluru; On-site; INR 1,800,000 - 2,400,000; Full time

Job summary

IndusInd Bank is seeking an experienced AI Product Manager to own the roadmap for AI and GenAI solutions, identify high-value use cases, build business cases, and drive enterprise-scale deployment from concept to realization.

You will partner with business and technology stakeholders to design AI workflows, ensure governance, measure adoption, and manage vendor ecosystems to keep the product aligned with enterprise standards.

Qualifications

• Bachelor's degree in Engineering, Computer Science, Information Technology, Data Science, or related discipline.
• Product management fundamentals: strategy, roadmaps, agile, requirements management, UX/design thinking.
• Generative AI, LLMs, RAG, AI workflows, governance concepts.
• Familiarity with AI risk, privacy, security, and compliance requirements.

Responsibilities

• Define and own AI product roadmap and identify high-value use cases.
• Partner with business stakeholders to translate requirements into features and AI capabilities.
• Design AI workflows, guardrails, governance requirements, and success metrics.
• Collaborate with Risk, Compliance, Legal, and Security for governance and controls.
• Define KPIs, monitor adoption, and report product performance.
• Manage vendor relationships and assess AI platforms and services.

Skills

Product strategy

Agile & Scrum

Requirements management

User experience & design thinking

Education

Bachelor's degree in Engineering/CS/IT/Data Science

Tools

AWS / Azure cloud platforms

API gateways

Data analytics platforms

Job description

• AI Product Strategy & Roadmap: Define and own the product roadmap for AI and GenAI solutions. Identify high-value AI use cases aligned with business objectives. Create business cases, benefit realization frameworks, and adoption plans. Prioritize opportunities based on value, feasibility, risk, and strategic alignment. Drive AI product vision from concept to enterprise-scale deployment
• Business Management : Partner with business stakeholders to understand pain points and opportunities. Translate business requirements into product features, user stories, and AI capabilities. Conduct workshops, discovery sessions, and design-thinking exercises. Build stakeholder alignment across business, technology, risk, and operations teams.
• AI Solution Design - Work closely with AI Engineers, Data Scientists, Architects, and Platform teams. Define AI workflows, user journeys, guardrails, governance requirements, and success metrics. Evaluate AI models, copilots, agents, intelligent automation, and RAG-based solutions. Ensure solutions are practical, scalable, explainable, and aligned to enterprise standards.
• AI Governance & Risk - Collaborate with Risk, Compliance, Legal, Information Security, and Model Risk teams. Ensure compliance with enterprise AI governance frameworks. Define controls for responsible AI, privacy, explainability, auditability, and model monitoring. Assess AI use cases against regulatory and policy requirements.
• Product Performance & Adoption - Define KPIs and outcome-based success measures. Monitor product usage, adoption, user satisfaction, and business impact. Drive continuous improvement through experimentation and feedback loops. Develop executive dashboards and performance reporting.
• Vendor & Ecosystem Management - Evaluate AI vendors, platforms, and solution providers. Conduct proof-of-concepts and product assessments. Manage vendor relationships and implementation partners. Stay current with developments in AI, GenAI, agentic systems, and emerging technologies.

EDUCATION

Essential requirements: Bachelor's degree in Engineering, Computer Science, Information Technology, Data Science, or related discipline.

Product Management

• Product strategy and roadmap development
• Agile and Scrum methodologies
• Requirements management
• User experience and design thinking

AI & Technology

• Generative AI and Large Language Models (LLMs)
• Retrieval Augmented Generation (RAG)
• AI Agents and Agentic Workflows
• Machine Learning fundamentals
• Prompt Engineering concepts
• API-based architectures and integrations
• Data platforms and analytics ecosystems

Governance & Risk

• AI risk and control frameworks
• Data privacy and security concepts
• Model monitoring and observability
• Regulatory and compliance awareness

Preferred:

• Experience with AI copilots and enterprise knowledge assistants.
• Understanding of MLOps, LLMOps, and AI Platform Engineering.
• Familiarity with AI gateway technologies and model management platforms.
• Experience building AI products in cloud environments.
• Knowledge of banking products and processes.
• Exposure to BIAN, enterprise architecture or digital transformation initiatives.