Applied AI Product Leader – Group Product Manager

Applied AI Product Leader – Group Product Manager

Job Description

Overview

The Applied AI Group Product Manager is a senior leadership role responsible for the overall product strategy, profit and loss (P&L), product team development, product operating model effectiveness, and objective attainment within a product area. This role focuses on creating the conditions that enable empowered product teams to solve complex customer problems that align with and solve for business needs. The Group Product Manager also performs product management individual contribution to one product in a product area. As an Applied AI leader, this role builds an operating model in which empowered teams harness AI and agentic accelerators—governed by eval guardrails—to compress concept-to-cash lead times. Applied AI leaders keep strategic decision-making firmly human and harness AI to enable rapid emergence of the right product, in the right way, at the right time.

Key Responsibilities

Required Qualifications

Preferred Qualifications

Core Competencies

• Strategic Vision: Develop, execute, and continually evolve a strategic vision for the product area.
• Leadership: Lead and inspire cross-functional teams, fostering collaboration and collective movement toward product goals.
• Customer-Centricity: Deep understanding of customer needs and engagement patterns, driving teams to deliver solutions that customers love and that work for the business.
• Continuous Improvement: Drive continuous improvement in product management practices and the product engineering operating model to enhance market value realization.
• Outcome Orientation: Focus on achieving outcomes over outputs and demonstrate the value of this approach through action and evidence.
• AI Agentic Fluency: Orchestrates and coaches the responsible use of AI agents across the concept-to-cash flow, with guardrails at each hand-off—assumptions, confidence levels, and links to sources of truth.
• AI Realism and Eval Fluency: Champions an evaluation harness across the product area—evals before launch, drift monitoring after, and a kill-switch mentality; knows the difference between deterministic logic and probabilistic generation, and when not to use AI.

Conclusion