[Hiring] Lead Product Manager, AI Platform @Global Payments Inc.

Role Description

Global Payments is investing in AI across its software and payments portfolio. We're hiring a Lead Product Manager, AI Platform to own the shared foundation that product teams across the company use to build AI experiences. You'll set the platform strategy, make build-vs-buy decisions, and test ideas with your own working prototypes before teams commit engineering capacity.

This is a platform role. You won't ship a single feature. You'll build the foundation that many AI products are built on. Your direct customers are the product and engineering teams building on the platform, and your success is measured by the value they deliver to the businesses we serve.

What You'll Own

• Platform strategy and economics
  • Define the multi-year vision and roadmap for a shared AI platform, and make it the default path for teams building AI capabilities.
  • Lead build-vs-buy-vs-partner decisions for core platform components, with clear trade-offs on portability, lock-in, cost, latency and security.
  • Own the model portfolio and AI unit economics, including routing rules, cost per interaction and pricing inputs to the business.
  • Manage relationships with cloud and model vendors, including roadmap influence and commercial terms.
• Platform capabilities and standards
  • Own the platform's core building blocks, such as orchestration, tools, workflows, connectors and agents, and be clear about when each pattern is the right one.
  • Define standards for how capabilities are exposed and integrated, including interfaces, schemas, error handling, permissions and safe handling of actions that change data.
  • Own how context, memory, identity and data access work across tenants and use cases.
• Quality, trust and governance
  • Build an evaluation practice that combines offline test sets, automated and human review, production quality signals and release gates.
  • Own guardrails and responsible-AI controls suited to a regulated payments environment, including data privacy, AI-specific security threats, grounding, action authorization and auditability.
  • Set production targets for quality, latency, availability and cost, and own the incident and rollback playbooks.
• Hands-on prototyping and discovery
  • Build working prototypes yourself, taking a customer problem to a demoable prototype in one to two weeks without dedicated engineering support.
  • Ship every prototype with a small evaluation set, a cost estimate, a latency measurement and a short note on what it would take to productionize.
  • Treat prototypes as evidence for decisions, not production systems, and keep a clear record of each problem, prototype and decision.
• Adoption and cross-functional leadership
  • Drive platform adoption across product and engineering teams, and run platform reviews with architecture, security, legal and risk.
  • Explain platform direction to senior leaders in plain language, and write the documents that keep a multi-team program aligned.
  • Mentor other product managers on AI product practice as an individual contributor leader.

Qualifications

• 8+ years of product management experience, with a meaningful share of that time on technical, platform or developer-facing products.
• A proven track record of shipping AI-powered products to production, including generative AI or agentic capabilities used by real customers, not only pilots.
• Strong grasp of how LLM-based systems behave in production, including non-determinism, failure modes, and quality, cost and latency trade-offs.
• Hands-on prototyping: you've personally built and demoed working AI prototypes, not only written specs.
• Working proficiency in Python (or a similar language), SQL, APIs and JSON.
• You're comfortable reading code, logs and evaluation results.
• Practical experience with at least one major cloud AI platform.
• A track record of designing AI evaluation and guardrail approaches and using them to make release decisions.
• Proven ability to align senior stakeholders in a matrixed organization, and to write clearly for both executives and engineers.
• Bachelor's degree in computer science, engineering, a quantitative field, or equivalent practical experience.

Preferred Qualifications

• Experience in payments, fintech, commerce or B2B SaaS.
• Experience running multi-tenant AI in regulated environments (e.g., PCI DSS, SOC 2, GDPR/CCPA).
• Has shipped AI that takes actions in production systems, with confirmation, undo and audit controls.
• Has built or governed a tool or integration catalog, including publishing and versioning standards.
• Experience with real-time or conversational AI, or with on-device and edge AI.
• Experience with multi-cloud AI architectures.
• Patents, open-source contributions, publications or talks on applied AI.
• Relevant cloud or AI certifications (a plus, not a requirement).

EEOC Statement

Global Payments is an equal opportunity employer. We evaluate qualified applicants without regard to race, color, religion, sex, sexual orientation, gender identity, marital status, genetic information, national origin, disability, veteran status, and other protected characteristics. The EEO is the Law poster is available here.

If you are made a conditional offer of employment and will be working in the United States, you will be required to undergo a drug test. In developing this job description care was taken to include all competencies and requirements needed to successfully perform the position. Reasonable accommodations will be provided for individuals with qualified disabilities both during the hiring process, as well as to allow the individual to perform the essential functions of the job, if hired.

Washington Candidates: Click here for information on the Fair Chance Act.