AI Product Manager

Lead/Senior Product Manager - Enterprise AI
Overview

Our client is undergoing a transformation to become an AI-first enterprise and is seeking a hands-on, technically proficient Product Manager to design, prototype, and scale AI-powered solutions across the organization.

In this role, you will partner with engineers, designers, data scientists, and business stakeholders to solve complex enterprise challenges through AI. You will own an AI product or major capability within the organization's AI portfolio, driving the full product lifecycle from business discovery and solution design through prototyping, evaluation, governance, and production deployment.

The ideal candidate combines strong product management expertise with hands-on experience leveraging modern AI tools, frameworks, and development approaches to rapidly validate ideas and deliver measurable business outcomes.
Key Responsibilities
Product Strategy & Roadmap

• Define the vision, strategy, and roadmap for AI products and capabilities.
• Evaluate competing priorities and make informed investment and prioritization decisions.
• Establish success metrics and align product outcomes to business objectives.
• Adapt product direction as business needs, technologies, and AI capabilities evolve.

Business Discovery & Opportunity Assessment

• Partner with stakeholders to understand business processes, operational challenges, and opportunities for AI adoption.
• Translate ambiguous business problems into clearly defined AI use cases with measurable value propositions.
• Develop business cases, ROI models, and success criteria to support investment decisions.

Hands-On AI Prototyping

• Develop working prototypes, proofs of concept, and demonstrations using modern AI development tools such as Claude Code, Cursor, OpenAI Codex, and related technologies.
• Validate technical feasibility, business value, and user adoption before committing full engineering resources.
• Rapidly iterate based on stakeholder feedback and evaluation results.

Product Definition & Requirements

• Convert validated concepts into actionable product requirements, user stories, acceptance criteria, edge cases, and success metrics.
• Collaborate with design teams to ensure intuitive, user-centric experiences that drive adoption.
• Clearly define what successful delivery looks like for engineering and data science teams.

Delivery & Execution

• Lead products from prototype through production deployment and ongoing enhancement.
• Own backlog management, prioritization, roadmap execution, and release planning.
• Make tradeoff decisions across scope, timelines, model capabilities, and business priorities.
• Drive momentum and execution across cross-functional teams.

AI Evaluation & Quality Management

• Define and implement evaluation frameworks to measure solution quality and business impact.
• Build and maintain test datasets, ground truth data, and evaluation methodologies.
• Utilize metrics including accuracy, precision, recall, F1 score, task success rate, and hallucination rate.
• Use evaluation outcomes to inform launch decisions, model improvements, and executive reporting.

AI Solution Design

• Partner with engineering teams on architectural discussions and technical feasibility assessments.
• Contribute to prompt engineering, agent workflows, retrieval strategies, and AI solution design patterns.
• Support implementation of scalable and reusable AI capabilities.

Cross-Functional Leadership

• Lead and influence engineers, data scientists, designers, and business stakeholders without direct authority.
• Facilitate alignment across technical and non-technical teams.
• Develop reusable frameworks, playbooks, and best practices for AI product delivery.

Governance & Responsible AI

• Support enterprise AI governance processes, reviews, and approvals.
• Prepare required documentation, scorecards, risk assessments, and stage-gate materials.
• Ensure compliance with responsible AI, security, privacy, and regulatory requirements.

Communication & Stakeholder Management

• Communicate product strategy, roadmap, ROI, and performance metrics to both technical and executive audiences.
• Present recommendations and business cases to senior leadership.
• Mentor team members and help elevate AI product management capabilities across the organization.

Continuous Learning & Innovation

• Stay current on advances in AI, agent frameworks, evaluation methodologies, GraphRAG, knowledge graphs, and emerging AI development practices.
• Identify opportunities to apply new technologies to business challenges.

Required Qualifications

• Bachelor's or Master's degree in Computer Science, Engineering, Information Systems, or a related field (or equivalent experience).
• 5-7+ years of product management or related experience, including experience delivering AI/ML or Generative AI solutions.
• Experience leading products from concept through production deployment.
• Hands-on experience using AI development tools and building prototypes or proof-of-concepts.
• Working knowledge of:
  • Large Language Models (LLMs)
  • Retrieval Augmented Generation (RAG)
  • AI Agents
  • Prompt Engineering
  • AI Evaluation Methodologies
• Experience building business cases, ROI models, and product roadmaps.
• Ability to communicate effectively across both business and technical audiences.
• Strong systems-thinking, problem-solving, and prioritization skills.
• Experience working in cross-functional environments with engineering, design, and data science teams.

Preferred Qualifications

• Experience within healthcare, insurance, financial services, utilities, or other regulated industries.
• Hands-on experience with:
  • Agent frameworks
  • GraphRAG and knowledge graph technologies
  • Reusable AI skills, plugins, or orchestration frameworks
  • Python development
• Knowledge of responsible AI, governance, privacy, and compliance requirements.
• Experience establishing AI evaluation frameworks and quality metrics.
• Strong point of view on the future of agentic AI, AI-native product development, and enterprise AI adoption.
• Ability to thrive in fast-paced, highly ambiguous environments.