AI Product Manager

American Express•Phoenix, AZ

About The Position

Enterprise Data Management & Products (EDMP) is a critical part of the Technology organization, focused on delivering data products that drive business growth and value, alongside developing strong data management strategies and governance solutions. The AI Product Manager is a hands-on technical leader responsible for identifying, designing, building, and scaling AI-powered products, agents, copilots, and workflow automation solutions within EDMP. This role bridges Product, Technology, and Business Operations, requiring comfort with both product discovery with business partners and prototyping AI solutions. The ideal candidate will translate complex business challenges into practical AI solutions, build and test early prototypes, and then collaborate with engineering teams for production and scaling. This is a technical product leadership role that emphasizes building working solutions, user testing, impact measurement, and scaling successful patterns across the enterprise, aligning with the AI Innovation Group's goal of moving EDMP from AI experimentation to responsible, enterprise-scale adoption.

Requirements

5+ years of experience in Technical Product Management, Product Management, Solution Engineering, AI Product Management, Software Development, Technical Consulting, or a related field.
Demonstrated experience delivering AI-enabled products, enterprise software, workflow automation, or digital platforms.
Hands-on experience building or configuring AI agents, copilots, chatbots, AI applications, or workflow automation solutions.
Demonstrated ability to independently prototype AI-powered solutions before partnering with engineering teams for production deployment.
Strong product discovery skills with the ability to translate ambiguous business problems into practical technical solutions.
Experience working directly with users and business stakeholders to test ideas, gather feedback, and iterate quickly.
Experience defining success metrics, evaluating AI solution performance, and measuring business value.
Ability to communicate effectively across technical and non-technical audiences and operate comfortably across Product, Technology, and Business teams.
Hands-on experience building solutions with one or more enterprise GenAI platforms such as OpenAI/ChatGPT Enterprise, Microsoft Copilot Studio/Azure AI, Anthropic Claude, or comparable platforms.
Ability to independently prototype AI applications using Python or a comparable programming language.
Experience integrating APIs, enterprise data sources, and workflow automation platforms.
Familiarity with modern development environments and Git-based development workflows.
Practical understanding of prompt engineering, retrieval-augmented generation (RAG), agent orchestration, tool/function calling, and emerging agentic AI patterns.
Experience designing or implementing evaluation approaches for AI applications, including assessing output quality, grounding, reliability, and user experience.
Understanding of enterprise AI considerations including security, privacy, governance, responsible AI, and production deployment.
Bachelor's degree in Computer Science, Engineering, Information Systems, Data Science, Business, or a related discipline, or equivalent professional experience.
Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.

Nice To Haves

Develop a strong understanding of EDMP workflows and identify a portfolio of high-value AI opportunities.
Rapidly turn promising ideas into working prototypes that can be tested with real users.
Personally build and validate AI agents, workflows, and applications rather than relying exclusively on engineering teams for early development.
Move the strongest solutions from prototype toward production in partnership with engineering and enterprise technology teams.
Demonstrate measurable improvements in productivity, cycle time, quality, user experience, or other business outcomes.
Develop reusable AI capabilities and patterns that allow successful solutions to scale across teams and use cases.
Help establish a repeatable approach for discovering, building, evaluating, and scaling AI solutions across EDMP.

Responsibilities

Identify and prioritize high-value opportunities where AI can improve business processes, employee experiences, productivity, or decision-making across EDMP.
Lead product discovery with business teams to understand workflows, pain points, user needs, and opportunities for AI-enabled transformation.
Translate business problems into clear AI product concepts, requirements, prototypes, and roadmaps.
Redesign workflows using AI-first principles, identifying where agents, copilots, automation, and human-in-the-loop experiences can create meaningful value.
Rapidly test ideas with users and iterate based on feedback, usage data, and solution performance.
Define success metrics and evaluation frameworks that measure solution quality, adoption, reliability, productivity gains, and business impact.
Personally design, build, and prototype AI-powered products, agents, copilots, knowledge assistants, and workflow automations.
Develop agentic workflows using enterprise GenAI platforms such as OpenAI/ChatGPT Enterprise, Microsoft Copilot Studio and Azure AI, Anthropic Claude, or comparable platforms.
Build solutions that leverage APIs, enterprise data sources, retrieval-augmented generation (RAG), tools, connectors, and workflow orchestration.
Use Python or comparable development tools to rapidly prototype applications, integrations, and AI-enabled workflows.
Work comfortably with APIs, JSON payloads, authentication patterns, Git-based development workflows, and modern developer environments.
Design and test prompts, agent instructions, tool usage, retrieval strategies, and orchestration patterns.
Build evaluation approaches to assess AI output quality, reliability, grounding, and user experience.
Experiment with emerging AI capabilities and determine where they can practically solve enterprise business problems.
Partner closely with engineering, architecture, data, security, risk, and business teams to move successful prototypes into production.
Remain actively involved in solution design, testing, evaluation, and iteration as solutions move from prototype to enterprise deployment.
Identify reusable agent capabilities, integrations, and solution patterns that can support multiple teams and use cases.
Establish practical design patterns, implementation playbooks, evaluation approaches, and best practices that accelerate responsible AI adoption.
Help teams navigate enterprise considerations including data access, security, privacy, governance, reliability, and responsible AI.
Share learnings from experiments and production deployments to continuously improve how EDMP designs and delivers AI solutions.