Head AI Product Manager

Job Title: Head AI Product Manager Experience: 12–15+ Years (with 7+ years in AI Product Management) Location: Navi Mumbai About Jio-bp Jio-bp (Reliance BP Mobility Limited) is a joint venture between Reliance Industries Limited (RIL) and bp, committed to transforming the mobility and energy landscape in India. Jio-bp delivers world-class fuels, mobility solutions, and customer experiences while advancing sustainability and digital innovation. As part of its digital transformation journey, Jio-bp is establishing an AI Center of Excellence (AI CoE) to drive data-led decision-making, innovation, and operational excellence across business functions.

Role Overview The Chief AI Product Manager will own and drive the enterprise AI/GenAI product portfolio for Jio-bp’s AI CoE—end-to-end from strategy to scale. This is a hands-on leadership role reporting to the Head of AI CoE, combining AI product management, business analysis, and program/project execution. You will lead a small team of Product Managers, run multiple delivery squads/scrums, and ensure production rollouts meet enterprise governance (Security/IRM), Responsible AI, and value realization expectations.

This role requires strong domain orientation (Retail + Oil & Energy a strong plus), the ability to translate business workflows into AI products, and strong analytical rigor to track outcomes, adoption, and cost.

Key Responsibilities 1.

Product

Strategy & Portfolio Ownership - Define AI CoE product vision, product operating model, and multi-quarter roadmap aligned to business priorities and enterprise platform readiness.

• Own portfolio-level prioritization and sequencing across BUs; ensure trade-offs across value, feasibility, risk, and timelines are made decisively.
• Establish portfolio OKRs/KPIs and value tracking: ROI, adoption, productivity gains, SLA improvements, and customer/operations impact. 1.

Business

Analysis & Problem Framing (Hands-on) - Lead enterprise business analysis for priority initiatives: workflow mapping, requirement elicitation, pain-point analysis, data readiness assessment, and success criteria definition.

• Convert ambiguous business asks into clear problem statements, user journeys, and product specs (PRDs/BRDs), backed by logical assumptions and measurable outcomes.
• Ensure data and process dependencies are captured early (source systems, data quality, refresh cycles, user roles, and control requirements). 1.

Delivery

Leadership (Squads, Scrums, and Program Governance) - Drive multi-squad execution: run or oversee scrums, sprint planning, reviews, retros, and delivery cadence across products.

• Own program governance: milestones, dependency management, RAID logs, release readiness, and escalation closure.
• Ensure high-quality delivery artifacts: epics/stories, acceptance criteria, UAT strategy, rollout/hypercare plans, and documentation standards.
• Governance, Risk, and Enterprise Readiness - Ensure enterprise readiness for every release: Security/IRM alignment, auditability, approvals, privacy-by-design, Responsible AI controls, and Dev→UAT→Prod release gates.
• Partner with AI Architects/MLOps/Cloud/Security teams to ensure solutions meet non-functional requirements: reliability, observability, controls, and supportability.
• Drive product standards for Responsible AI and GenAI safety: guardrails, evaluation evidence, monitoring expectations, and controlled change management.
• Adoption, Change Management & Stakeholder Leadership - Lead change management at scale: stakeholder communications, enablement/training, rollout playbooks, and adoption measurement.
• Run executive updates and steering forums: crisp status, risks, mitigation, investment asks, and value realization reporting.
• Build strong partnerships with BU leaders, SPOCs, and operations owners to ensure sustained adoption and continuous improvement.
• GenAI Cost / Value Discipline - Own GenAI usage economics: define and track token usage, cost per interaction, cost-to-value, and consumption guardrails.
• Partner with Platform/MLOps/FinOps to implement cost controls and reporting—ensuring scalable rollouts without cost overruns. 1.

People

Leadership (Small Team Management) - Lead and mentor a small team of AI Product Managers/BAs: capability building, quality standards, delivery ownership, and performance feedback.

• Establish consistent ways of working across internal and partner teams to drive speed, quality, and accountability. Must-Have Skills & Experience - Bachelor’s/Master’s degree in Engineering, Computer Science, Statistics, Mathematics, Economics, or related field; MBA or equivalent is a plus.
• 12–15+ years in Product/Program roles, with 7+ years owning AI/ML/GenAI products end-to-end, including multi-squad deliver