AI Product Manager

IndusInd Bank · Bengaluru

  • Experience2–7 yrs
  • SalaryNot disclosed
  • Work modeonsite
  • Levelmid
  • Posted15 Sept 2026
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About IndusInd Bank

IndusInd Bank is hiring in Bengaluru in financial services. This role looks for around 2+ years of experience.

Skills

  • Product Management
  • Product strategy
  • Product lifecycle management
  • Agile
  • Scrum
  • Business case development
  • Requirements management
  • Stakeholder management
  • User experience
  • Design thinking
  • Generative AI
  • Large Language Models
  • Retrieval Augmented Generation
  • AI Agents
  • Agentic Workflows
  • Machine Learning
  • Prompt Engineering
  • API-based architectures
  • Data platforms
  • Responsible AI
  • AI risk and control frameworks
  • Data privacy
  • Model monitoring
  • Regulatory and compliance awareness

The role

An AI product manager at a financial services company defines Generative AI roadmaps, shapes Retrieval Augmented Generation solutions, and establishes Responsible AI controls across enterprise deployments. The role also applies Agile, Scrum, and product strategy to align stakeholders, measure adoption, and guide vendor ecosystems.

Full job description

Overall, 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 deploymentBusiness 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.

EDUCATIONEssential requirements: Bachelor's degree in Engineering, Computer Science, Information Technology, Data Science, or related discipline.Preferred: MBA or equivalent business qualification preferred.

Essential requirements: Product ManagementProduct strategy and roadmap developmentProduct lifecycle managementAgile and Scrum methodologiesBusiness case developmentRequirements managementStakeholder managementUser experience and design thinkingAI & TechnologyGenerative AI and Large Language Models (LLMs)Retrieval Augmented Generation (RAG)AI Agents and Agentic WorkflowsMachine Learning fundamentalsPrompt Engineering conceptsAI Platforms (Azure AI Foundry, AWS Bedrock, Databricks, OpenAI, Anthropic, Google Vertex AI)API-based architectures and integrationsData platforms and analytics ecosystemsGovernance & RiskResponsible AI principlesAI risk and control frameworksData privacy and security conceptsModel monitoring and observabilityRegulatory and compliance awarenessPreferred: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.