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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 will accept applications for this role until at least 06\-12\-2026Job Description
The VP, Service Operations \& AI Management is responsible for leading the daily performance of a global, multi‑channel Client Care organization while overseeing the scaled operational use of AI across service delivery. This leader manages a hybrid workforce of human frontline teams and AI agents, ensuring safe, compliant, and value‑yielding use of automation and machine intelligence.
While accountable for service levels, efficiency, quality, and frontline leadership, this position must also operate as a digital workforce orchestrator, responsible for optimizing AI behavior, tuning digital workflows, and ensuring the service environment is designed for a highly automated future.
This role represents the next generation of operations leadership—where digital labor, AI governance, and human expertise converge to define the service experience of the future.
The role will report to the SVP of Client Care.
Key Responsibilities
1\. Operational Leadership
- Deliver daily service performance across all channels, ensuring SLAs, quality, and customer experience targets are met.
- Oversee frontline leadership, coaching routines, staffing execution, adherence, and vendor performance.
- Lead intraday management, quality execution, and operational efficiency programs.
- Manage complex global operations across multiple sites and time zones.
2\. AI Operations \& Governance
- Serve as the operational owner for AI within Client Care, ensuring responsible, safe, and compliant use of AI in service delivery.
- Lead and enable adoption of AI\-assist tools for all Client Care teams.
- Monitor, measure, and optimize AI performance (accuracy, containment, intent recognition, assist adoption).
- Identify operational risks and ensure AI agents adhere to guardrails, privacy standards, escalation paths, and human‑in‑the‑loop processes.
- Partner with Strategy \& Planning to refine AI standards, model requirements, and governance frameworks.
- Oversee AI workforce health, including training cycles, prompt tuning, behavior audits, exception handling, and bias detection.
3\. Change Delivery
- Translate the enterprise roadmap into executable operational work, ensuring new policies, journeys, tools, and AI solutions are embedded seamlessly into frontline environments.
- Identify execution risks, resource requirements, and change dependencies early.
- Share insights that inform experience design, prioritization, and product roadmaps.
4\. Digital Workforce \& Capacity Management
- Integrate AI agents into Workforce Management (WFM), forecasting, routing, and staffing models as part of a blended human \+ digital labor strategy.
- Manage digital agent quality, workload balancing, escalation logic, and reliability.
- Establish digital agent KPIs (e.g., accuracy, containment, compliance, efficiency) equivalent to human performance KPIs.
5\. People Leadership
- Lead and grow a global operations team capable of executing in a highly automated environment.
- Build new leadership capabilities including AI fluency, digital operations readiness, and continuous improvement disciplines.
- Foster a culture of customer obsession, innovation, data\-driven decision making, and operational excellence.
Key Competencies
- Digital \& AI Fluency: Understands AI agents, automation frameworks, model behavior, and digital service ecosystems.
- Enterprise Thinking: Designs operations with long\-term scalability, risk management, and cross\-functional alignment.
- Customer Experience Leadership: Uses insights, journey thinking, and experience design to elevate satisfaction and reduce effort.
- Advanced Operational Excellence: Deep knowledge of WFM, quality, performance management, and root\-cause analysis.
- AI Operational Governance: Skilled in monitoring, tuning, auditing, and governing AI systems in production.
- Data‑Driven Management: Uses real\-time data, predictive insights, and performance analytics to manage a dynamic operation.
- People Leadership \& Talent Development: Grows leaders who can operate effectively across human and AI workflows.
- Change Leadership: Leads through ambiguity, builds adoption for new capabilities, and drives cultural transformation.
- Influence \& Cross\-Functional Partnership: Partners with Product, Technology, Strategy \& Planning, Compliance, and Finance.
- Operational Decision\-Making: Makes fast, informed tradeoffs under pressure.
- Continuous Improvement Mindset: Implements scalable improvements across human and digital workflows.
- Risk \& Compliance Orientation: Ensures safety, regulatory adherence, and ethical use of AI.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.Qualifications
Basic Qualifications • 12 or more years of work experience with a bachelor’s Degree or at least 10 years of experience with an Advanced degree (e.g. Masters/ MBA/JD/MD) or at least 8 years of work experience with a PhD • 12\+ years in large\-scale contact center or service operations leadership, with at least 5 years at senior/executive level. • Demonstrated experience operationalizing AI and automation at scale. • Expertise in WFM, vendor management, quality programs, and multi site operations. • Proven track record leading transformation, including automation, digital channel enablement, or AI introduction. • Experience managing global teams and third party partners. • Bachelor’s degree required, Master’s degree preferred (Business, Operations, Analytics, or related discipline). Work Hours: • Incumbent must make themselves available during core business hours. This role is designated as a hybrid role, requiring in\-office attendance at minimum on Tuesdays, Wednesday and Thursdays and as business needs require, and working from a home office location on other days. Travel Requirements: • This position requires the incumbent to travel for work 20% of the time. 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, and reach with hands and arms. U.S. APPLICANTS ONLY: The estimated salary range for this position is 206,900 to 362,500 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.U.S. Applicants Only
The estimated salary range for this position is $206,900\.00 to $ 362,500\.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.
Salary Context
This $206K-$362K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Visa, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills in Demand for This Role
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. This role's midpoint ($284K) sits 57% above the category median. Disclosed range: $206K to $362K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Visa AI Hiring
Visa has 10 open AI roles right now. They're hiring across Data Scientist, AI Product Manager, AI/ML Engineer, AI Software Engineer. Positions span Foster City, CA, US, Austin, TX, US, Atlanta, GA, US. Compensation range: $163K - $362K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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