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About This Role
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 an 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.
At American Express, our mission is to deliver the world’s best customer experience every day. At the heart of this mission is our Information Security organization, enabling exceptional experiences built on a foundation of trust, service, and security. We leverage advanced technologies and data\-driven insights to stay ahead of an evolving threat landscape. We foster a culture of passion, curiosity, and courage—empowering you to innovate, grow, and help shape the future of a Fortune 100 company.
Trust. Service. Security.
The Data Security team is responsible for safeguarding the organization’s most critical asset—its data—by ensuring confidentiality, integrity, and availability across all platforms and environments. Working at the intersection of cybersecurity, data governance, and privacy, the Data Security team focuses on capabilities such as data classification, encryption, secure data access, and monitoring. The team partners closely with engineering, risk, and business units to embed security controls throughout the data lifecycle—from creation and storage to sharing and archival.
As a Product Owner, you will lead the development of platforms and capabilities that enable internal teams to securely leverage enterprise data encryption services. This role focuses on building and enhancing a services portal for encryption solutions, as well as products that integrate with Hardware Security Modules (HSMs). You will play a critical role in shaping the direction of encryption products—driving innovation, accelerating AI adoption, improving automation, and enhancing the efficiency and scalability of security services across the organization. You will champion the responsible use of AI across the product lifecycle, identifying opportunities to improve customer experiences, streamline engineering workflows, and enable secure AI\-powered capabilities within enterprise security platforms. You should have strong product mindset with the ability to translate complex technical capabilities into scalable, user\-focused solutions.
How will you make an impact in this role?
The Product Owner is accountable for translating product direction into precise, executable work while ensuring strong alignment with engineering delivery and measurable outcomes. This role emphasizes disciplined backlog management, clear prioritization, and consistent execution patterns to improve delivery predictability, reduce fragmentation, and enhance overall product quality. You will act as the voice of the customer and the steward of execution excellence—ensuring that encryption services are delivered securely, reliably, and with a high degree of operational rigor.
This role also plays a key part in enabling the evolution of the platform to support AI\-driven use cases—ensuring that encryption services are accessible, intuitive, and scalable for both human users and intelligent systems. The Product Owner will incorporate AI capabilities into product development in a disciplined, execution\-focused manner, aligning innovation with secure, reliable delivery.
Minimum Qualifications
- 5\+ years Product Owner or execution\-focused delivery experience.
- 7\+ years of experience working with Agile/Scrum teams in a fast\-paced development environment including experience driving execution discipline across multiple teams and improving delivery predictability and consistency.
- Proven experience managing backlogs and delivering software products end\-to\-end including ownership of complex, multi\-team backlogs with measurable delivery outcomes and reduced fragmentation.
- Comfortable engaging with engineering teams on technical requirements, dependencies, and constraints including deep partnerships with engineering on execution trade\-offs, scalability, and implementation consistency.
- Experience supporting multiple Scrum teams simultaneously (strongly preferred).
- Skilled in breaking down large initiatives into incremental, testable, and deliverable user stories.
- Ability to lead effective backlog refinement sessions, stakeholder discussions, and decision\-making meetings.
- Proven product mindset—ability to connect business value, user needs, and technical execution.
- Excellent communication, prioritization, and stakeholder management skills.
- Strong analytical thinking and ability to make data\-driven decisions.
- Demonstrated experience using Generative AI tools (e.g., ChatGPT, GitHub Copilot, Microsoft Copilot, Claude, Gemini, or similar) to improve product management, documentation, analysis, or software delivery workflows.
· Strong understanding of AI, Generative AI, Agentic AI, and Large Language Model (LLM) concepts, including opportunities, limitations, governance considerations, and secure enterprise adoption.
Bachelor Science in Computer Science or Information Technology or equivalent technical experience.
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Preferred Qualifications:
- Certified Scrum Product Owner (CSPO) or equivalent certification.
- Experience working in scaled Agile environments (SAFe or similar).
- Data\-driven mindset with experience using metrics to guide decisions.
- Experience leading AI adoption initiatives within product or engineering organizations, including integrating AI into product strategy, operational workflows, or software delivery practices.
- Experience defining AI\-powered product capabilities, including conversational interfaces, intelligent automation, AI agents, or retrieval\-augmented applications.
- Familiarity with responsible AI principles, model governance, prompt engineering, and secure AI implementation in regulated environments.
- Familiarity with encryption, tokenization, and other data security concepts.
- Experience working with enterprise security platforms or regulated environments.
Familiarity with programming languages (Java, ReactJs etc..) and concepts
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Employment eligibility to work with American Express in the United States is required as the company will not pursue visa sponsorship for these positions.
Salary Context
This $123K-$215K range is below the median for AI Product Manager roles in our dataset (median: $185K across 167 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 4,317 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At American Express, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $217,100 based on 471 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($169K) sits 22% below the category median. Disclosed range: $123K to $215K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
American Express AI Hiring
American Express has 10 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager. Positions span New York, NY, US, Phoenix, AZ, US, Sunrise, FL, US. Compensation range: $150K - $215K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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