Lead Technical Program Manager - GenAI Platform

About Us

Visa is a world leader in payments technology, facilitating transactions between consumers, merchants, financial institutions and government entities across more than 200 countries and territories, dedicated to uplifting everyone, everywhere by being the best way to pay and be paid.

At Visa, you'll have the opportunity to create impact at scale - tackling meaningful challenges, growing your skills and seeing your contributions impact lives around the world.

Join Visa and do work that matters - to you, to your community, and to the world. Progress starts with you.

Visa is hiring an experienced Technical Program Manager to help build, operate, and evolve Visa's GenAI Platform, which provides centralized AI model inferencing capabilities for teams and applications across Visa.

The GenAI Platform enables enterprise use of generative AI across multiple models and hosting environments, including on-premises infrastructure and third-party providers. The platform must operate globally at scale while meeting the performance, privacy, operational, and compliance requirements of a global financial services company.

The TPM will work closely with product management, engineering, architecture, infrastructure, privacy and compliance stakeholders, third-party hosting and model providers, and teams across Visa to define platform capabilities, drive complex technical programs, and manage the lifecycle of AI models offered through the platform. The role will also help advance how Visa uses AI through capabilities such as intelligent model routing, performance optimization, and more efficient use of models and tokens.

• GenAI platform development:
  Drive major initiatives to build and evolve Visa's centralized inference platform. Work across engineering, architecture, infrastructure, and platform consumers to translate platform needs into clear, executable programs.
• Global scale and performance:
  Drive programs to scale the platform for global use across Visa. Partner with engineering to optimize inference performance, capacity, resilience, and operational efficiency as adoption and workloads grow.
• Model hosting and infrastructure:
  Coordinate programs supporting multiple model deployment and hosting approaches, including on-premises model hosting and third-party infrastructure. Develop sufficient technical depth to understand architecture, infrastructure, capacity, performance, and operational dependencies and drive them to resolution.
• Model onboarding and lifecycle:
  Drive the end-to-end process for bringing new AI models onto the platform and managing them through their lifecycle, including technical integration, evaluation, operational readiness, ongoing management, and required compliance activities.
• Third-party providers:
  Coordinate technical and operational dependencies with external model and hosting providers. Ensure provider capabilities, changes, constraints, and dependencies are understood and incorporated into platform plans.
• Privacy and compliance:
  Partner with engineering, privacy, compliance, and other stakeholders to ensure the platform, models, and operating processes support the privacy and compliance requirements associated with deploying and operating generative AI within a global financial services company.
• Advanced AI capabilities:
  Work with engineering and architecture to research and develop emerging platform capabilities such as intelligent model routing and other techniques that improve model selection, performance, efficiency, and application outcomes.
• AI efficiency and cost optimization:
  Drive initiatives that improve the efficiency of AI usage across Visa, including reducing unnecessary token consumption, optimizing model selection and application usage patterns, and identifying opportunities to lower inference costs while meeting application requirements.
• Platform adoption:
  Work with teams across Visa integrating with the GenAI Platform to understand their requirements, identify common needs, remove adoption barriers, and translate recurring needs into scalable platform capabilities.
• Problem definition and success criteria:
  Establish clear problem statements, scope, expected outcomes, and measurable success criteria for major platform initiatives. Build shared understanding across stakeholders before significant engineering work begins.
• Technical program planning and execution:
  Translate complex platform initiatives into clear requirements, dependencies, milestones, success…