Product Manager - Genius AI Platform

Global Payments Inc.•Charlotte, NC

About The Position

Global Payments is hiring a Lead Product Manager, AI Platform to own the shared foundation that product teams use to build AI experiences across its software and payments portfolio. The role sets platform strategy, makes build-versus-buy decisions, and develops working prototypes to test ideas before engineering teams commit capacity. It is a platform role focused on enabling other products rather than shipping a single feature. The platform’s direct customers are product and engineering teams, and success is measured by the value those teams deliver to the businesses Global Payments serves. The position owns the platform vision and roadmap, platform economics and model portfolio, vendor relationships, core AI capabilities and standards, and cross-tenant approaches to context, memory, identity and data access. It also establishes AI evaluation practices, responsible-AI controls, production targets, and incident and rollback procedures. The role requires hands-on prototyping, broad adoption across product and engineering teams, cross-functional reviews with architecture, security, legal and risk, clear communication with senior leaders, and mentoring other product managers as an individual contributor leader.

Requirements

8 or more years of product management experience, with a meaningful portion focused on technical, platform or developer-facing products.
Proven track record of shipping AI-powered products to production, including generative AI or agentic capabilities used by real customers, not only pilots.
Strong understanding of how LLM-based systems behave in production, including non-determinism, failure modes, and quality, cost and latency trade-offs.
Hands-on experience personally building and demoing working AI prototypes.
Working proficiency in Python or a similar language, SQL, APIs and JSON, with comfort reading code, logs and evaluation results.
Practical experience with at least one major cloud AI platform.
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 write clearly for executives and engineers.
Bachelor's degree in computer science, engineering, a quantitative field, or equivalent practical experience.

Nice To Haves

Experience in payments, fintech, commerce or B2B SaaS.
Experience running multi-tenant AI in regulated environments, such as PCI DSS, SOC 2 or GDPR/CCPA.
Experience shipping AI that takes actions in production systems with confirmation, undo and audit controls.
Experience building or governing 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.

Responsibilities

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-versus-buy-versus-partner decisions for core platform components, weighing portability, vendor 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.
Own platform building blocks such as orchestration, tools, workflows, connectors and agents, and define when each pattern is appropriate.
Define standards for exposing and integrating capabilities, including interfaces, schemas, error handling, permissions and safe handling of data-changing actions.
Own how context, memory, identity and data access work across tenants and use cases.
Build an AI evaluation practice combining offline test sets, automated and human review, production quality signals and release gates.
Own responsible-AI guardrails and controls for 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 incident and rollback playbooks.
Build working prototypes independently, taking customer problems to demoable prototypes in one to two weeks without dedicated engineering support.
Deliver each prototype with a small evaluation set, cost estimate, latency measurement and note on productionization requirements.
Use prototypes as evidence for decisions rather than production systems, and maintain a record of each problem, prototype and decision.
Drive platform adoption across product and engineering teams and run platform reviews with architecture, security, legal and risk.
Explain platform direction to senior leaders and write documents that align a multi-team program.
Mentor other product managers on AI product practice as an individual contributor leader.