Lead Technical Program Manager - GenAI Platform

Visa•Foster City, CA

15d•$192,300 - $307,600•Hybrid

About The Position

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.

Requirements

10 or more years of work experience with a Bachelor’s Degree or at least 8 years of work experience with an Advanced Degree (e.g. Masters/ MBA/JD/MD) or at least 3 years of work experience with a PhD
12 or more years of work experience with a Bachelor’s Degree or 8-10 years of experience with an Advanced Degree (e.g. Masters, MBA, JD, MD) or 6+ years of work experience with a PhD
Master’s degree in computer science, engineering, data science, artificial intelligence, or a related technical or quantitative field is required.
You have built software or operated complex technical systems.
You can read architecture diagrams, understand API contracts, reason about distributed systems and data flows, and have substantive technical conversations with engineers as a peer, not a spectator.
You have driven complex, cross-team engineering programs from ambiguity through production delivery, including planning, dependencies, risks, integration, and operational readiness.
You can develop a strong working understanding of AI model serving, infrastructure, APIs, capacity, performance, observability, and application integration without needing to be the engineer designing each component.
You understand the challenges of building shared technology platforms used by many engineering teams and can distinguish individual application requirements from capabilities that belong in a reusable enterprise platform.
You understand the challenges of operating large-scale, globally distributed technology platforms and can drive programs spanning performance, capacity, reliability, and operational maturity.
You can drive complex programs involving engineering teams, internal stakeholders, and external technology providers without relying on direct organizational authority.
You can translate engineering complexity into business impact and business needs into clear technical outcomes.
Academic or research experience focused on the practical application of artificial intelligence, particularly the use of AI in decision-making, optimization, or complex problem solving.
Advanced academic work in artificial intelligence, machine learning, or related disciplines.
Experience with generative AI, large language models, AI platforms, model inference, or machine learning infrastructure.
Experience building or operating technology in a regulated environment.
Technical depth in one or more of the following: AI/LLMs, model serving and inference, APIs and distributed systems, cloud and on-premises infrastructure, Kubernetes, platform engineering, CI/CD and DevOps, observability, performance engineering, analytics, or developer tooling.
Experience with Visa or the payments industry.

Responsibilities

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.
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.
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, operational dependencies and drive them to resolution.
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.
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.
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.
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.
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.
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.
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.
Translate complex platform initiatives into clear requirements, dependencies, milestones, success criteria, and execution plans.
Drive programs across organizational boundaries from ambiguity through production delivery.
Identify and manage dependencies across the GenAI Platform, infrastructure teams, third-party providers, platform consumers, and other Visa technology organizations.
Partner with engineering to continually improve the reliability, observability, quality, performance, and operational maturity of the platform and the models it provides.
Drive improvements in engineering planning, automation, quality, delivery practices, and metrics.
Help identify opportunities to use AI to improve engineering productivity.
Identify technical, delivery, privacy, compliance, provider, and organizational risks early.
Establish clear ownership and drive cross-functional issues to resolution.
Provide clear, concise communication about priorities, technical decisions, dependencies, delivery status, and risks to engineering teams and senior stakeholders.
Ensure engineering plans and platform commitments reflect technical realities, dependencies, and available capacity.
Maintain alignment among platform consumers, product management, and engineering.