Asks for

The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“Experience with generative AI, large language models, AI platforms, model inference, or machine learning infrastructure.”
The ad says“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.”
The ad says“Experience with generative AI, large language models, AI platforms, model inference, or machine learning infrastructure.”
The ad says“Experience with generative AI, large language models, AI platforms, model inference, or machine learning infrastructure.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”
The ad says“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.”

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.

Job Description

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.
Key Responsibilities:

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

Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.

Qualifications

Basic Qualifications:

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

Preferred Qualifications:

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
Education. Master’s degree in computer science, engineering, data science, artificial intelligence, or a related technical or quantitative field is required.
Engineering background. 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.
Delivery track record. You have driven complex, cross-team engineering programs from ambiguity through production delivery, including planning, dependencies, risks, integration, and operational readiness.
Technical depth. 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.
Platform mindset. 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.
Scale and operations. You understand the challenges of operating large-scale, globally distributed technology platforms and can drive programs spanning performance, capacity, reliability, and operational maturity.
Cross-functional leadership. You can drive complex programs involving engineering teams, internal stakeholders, and external technology providers without relying on direct organizational authority.
Communication. You can translate engineering complexity into business impact and business needs into clear technical outcomes.
AI research and decision-making. 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.

Information for US Applicants

For roles located in the US, the estimated salary range for this position is $192,300.00 to $ 307,600.00 USD per year, which may include potential sales incentive payments (if applicable). Salary may vary depending on job-related factors which may include knowledge, skills, experience, and location. In addition, this position may be eligible for bonus and equity.Visa has a comprehensive benefits package for which this position may be eligible that includes Medical, Dental, Vision, 401(k), FSA/HSA, Life Insurance, Paid Time Off, and Wellness Program.

Work Hours

Varies upon the needs of the department.

Travel Requirements

This position requires travel 5-10% of the time.

Mental/Physical Requirements

This position will be performed in an office setting. The position will require the incumbent to sit and stand at a desk, communicate in person and by telephone, frequently operate standard office equipment, such as telephones and computers.

Visa is an EEO Employer

Qualified applicants will receive consideration for employment without regard to race, color religion, sex, national origin, sexual orientation, gender identity, disability or protect veteran status. Visa will also consider for employment qualified applicants with criminal histories in a manner consistent with the EEOC guidelines and applicable local law.