Director-Digital Product Management - AI Enterprise Platform

Joining Amex Tech means discovering and shaping your contribution to something big. Here, you can work alongside talented tech teams and build a unique career with the Powerful Backing of American Express. With a range of opportunities to work with the latest technologies, and a commitment to back the broader engineering community through open source, our mission is to power your success. Because Amex Tech is powered by our technology, our culture, and our colleagues.

The Technology organization enables and accelerates the company’s growth strategies, delivering global capabilities and services in support of Amex’s customers and colleagues, while maintaining 24/7 servicing and availability to ensure uninterrupted, high-quality customer experience. Technology provides the foundation for everything we do in the company while driving differentiation through building and leveraging innovative technology and data insights.

The Enterprise AI Platform organization partners with business and technology teams to shape the Enterprise AI strategy, bring emerging technologies to the forefront, and build scalable, reusable AI platforms. This team plays a pivotal role in enabling American Express to operate as an AI-powered enterprise by equipping teams with the skills, platforms, and seamless deployment capabilities needed to accelerate responsible AI innovation, enhance decision-making, and drive customer impact with speed and scale.

Innovation remains central to evolving our Enterprise AI Platforms. The Applied AI Innovation function will evaluate and apply emerging AI capabilities and partnerships to shape future platform capabilities and accelerate enterprise adoption

The Enterprise AI Platform team builds the foundational capabilities that enable teams across American Express to develop, deploy, govern, and scale AI-powered products and experiences securely and responsibly.

We are seeking an experienced Director of Product Management to own the strategy, multi-year roadmap, investment priorities, delivery, adoption, and measurable outcomes for a portfolio within the Enterprise AI Platform

This leader will determine where enterprise investment can remove common barriers to AI development, translate repeated customer and FDE learnings into reusable platform capabilities, and make deliberate build, buy, partner, and standardization decisions. The goal is to make it materially faster, easier, and more cost-effective for teams across American Express to move AI use cases from experimentation to secure, scaled production.

The Director will partner closely with Engineering, Architecture, Data Science, Security, Risk, and business teams while leading product managers responsible for delivering enterprise-scale AI capabilities.

What Success Looks Like

Success means establishing a clear product strategy and investment thesis for the portfolio; increasing adoption and reuse of common AI platform capabilities; reducing the time, cost, and complexity required to build and operate AI applications; and demonstrating measurable improvements in developer productivity, platform economics, and business value enabled.

Product Strategy & Discovery

• Own the product vision and strategy for a portfolio within the Enterprise AI Platform.

• Identify recurring customer needs and translate enterprise AI and FDE learnings into scalable platform opportunities, distinguishing capabilities that should be standardized and reused from requirements that should remain application-specific.

• Identify opportunities to simplify the AI developer journey and establish scalable "paved roads" while preserving teams' ability to innovate.

• Evaluate emerging AI technologies and ecosystem shifts based on their potential to improve customer outcomes, developer productivity, platform economics, enterprise reuse, and speed to production.

Roadmap & Investment Prioritization

• Own the product roadmap and prioritize investments based on customer value, developer productivity, technical feasibility, scalability, enterprise reuse, platform economics, and business value enabled.

• Lead product trade-offs and build-versus-buy-versus-partner decisions, including when capabilities should be developed internally, sourced from technology partners, or adopted from open-source ecosystems.

• Make deliberate choices about where the platform should provide enterprise standards and abstractions versus where product teams should retain flexibility.

• Align Product, Engineering, Architecture, Security, Risk, and business stakeholders around priorities, investments, and product outcomes.

Product Development & Delivery

• Lead the product lifecycle from discovery through delivery in close partnership with Engineering, Architecture, and Forward-Deployed Engineering teams.

• Deliver secure, scalable, and reliable platform capabilities, APIs, developer tooling, and reusable AI services that reduce the effort required for teams to build and operate AI applications.

• Balance innovation and speed with enterprise requirements for security, privacy, risk, responsible AI, reliability, and operational excellence.

Launch, Adoption & Scale

• Partner with Forward-Deployed Engineering to accelerate adoption, solve high-value enterprise use cases, and identify platform gaps through direct engagement with internal customers.

• Translate FDE implementations and customer learnings into reusable platform capabilities and patterns, while distinguishing repeatable enterprise needs from use-case-specific requirements.

• Drive adoption and reuse across engineering teams and business units by improving developer experience and removing barriers to moving AI applications from experimentation to production.

Measurement & Continuous Improvement

• Define product success metrics across adoption and reuse, developer productivity, time-to-production, platform health and reliability, platform economics, customer satisfaction, and business value enabled.

• Use customer feedback, platform telemetry, and FDE insights to continuously improve products and inform roadmap and investment decisions.

• Measure whether platform investments reduce duplicated engineering effort, improve reuse, accelerate delivery, or improve the economics of developing and operating AI applications.

Product & People Leadership

• Build and lead a high-performing product organization, developing product managers and future product leaders while establishing strong operating mechanisms, accountability, and execution excellence.

• Establish strong product practices and decision frameworks that drive strategic clarity and effective product decisions.

• Build a culture of customer focus, technical excellence, collaboration, and continuous learning.

• 10+ years of progressive product management experience, including leadership experience building enterprise software, platforms, developer products, AI/ML products, or cloud technologies.

• Proven experience defining product strategy and leading multi-year roadmaps for complex enterprise platforms.

• Demonstrated success building and scaling products that enable other teams to develop differentiated customer or employee experiences.
• Strong understanding of modern AI application architecture and the platform capabilities required to build, evaluate, deploy, govern, observe, and operate AI systems at enterprise scale.
• Experience partnering deeply with Engineering to deliver highly technical platform capabilities.
• Proven ability to influence senior stakeholders and drive alignment across complex, matrixed organizations.
• Strong product judgment, including the ability to determine what should be standardized, abstracted, reused, purchased, or left to individual product teams while balancing customer, technical, operational, risk, and business considerations.
• Excellent executive communication, storytelling, and presentation skills.

Preferred Qualifications

• Experience leading product strategy for enterprise AI platforms, developer tools, ML infrastructure, or intelligent automation.
• Experience building or scaling AI-powered products, LLM platforms, agentic systems, or AI orchestration capabilities.
• Familiarity with modern AI platform capabilities such as model access and gateways, agent and tool integration, evaluation, retrieval and knowledge systems, prompt management, and AI observability.
• Experience with enterprise AI governance, security, privacy, model risk management, and responsible AI.
• Experience developing partnerships with hyperscalers, AI model providers, enterprise technology companies, or open-source communities.
• Experience defining platform adoption strategies and measuring developer productivity, platform health, platform economics, and business value enabled.

Depending on factors such as business unit requirements, the nature of the position, cost and applicable laws, American Express may provide visa sponsorship for certain positions.