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About This Role
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
We are seeking a strategic AI deployment and automation expert to lead and support the transformation of agentic and AI\-enabled workflows in Advisory Services portfolios (Consulting \& Analytics and Marketing Services) and Advisory Services Finance team. In this role, you will be a strategic partner and hands\-on operator.
The scope of this role covers two primary areas of responsibility:
- Strategic partner for AI leaders in Advisory Services to shape and support the AI roadmap, and
- Leading the financial planning and analysis (FP\&A) process reimagination and AI deployment for the finance team supporting Value Added Services (VAS).
Advisory Services AI strategic support: You will partner with cross\-functional stakeholders to shape the strategy and execute on the rollout plan, track and measure the impact in Advisory Services operational leverage and KPIs, and evaluate further investments required to meet the long\-term vision.
Leading Advisory Services Finance AI deployment: You will review the process design and identify opportunities for automation and workflow optimization, driving prototyping and solution design, and deploying agentic AI\-enabled workflows. The objective is to implement impactful agentic solutions for reporting and planning cycles and launch AI assistants supporting VAS FP\&A that can respond from structured and unstructured data. You will help shape the integration bridges between AI tools and enterprise systems and agentic workflow models to unlock AI‑enabled capabilities.
Successful candidate will have strong knowledge of AI solutions, automation suites, business processes and financial systems.
The ideal candidate would have a bias toward action, and the ability to balance getting the details right while moving fast with seamless execution.
You will gain invaluable experience in a position that offers significant responsibility and interaction with senior finance and cross\-functional partners as well as considerable opportunity to have a large impact within a data\-driven Finance organization. Our ideal candidate is a well\-rounded top performer who can be a leader in a dynamic, high\-energy, and ambiguous environment.
Key Responsibilities:
- Own strategic support for Advisory Services AI initiatives, and lead evaluation of investment business cases for AI initiatives across Consulting \& Analytics and Marketing Services.
- Define and implement scalable AI\-enabled use cases and agentic workflows that auto\-generate finance/business intelligence deliverables across cycles for month\-end and quarter\-end close, and business planning processes for budget and outlook.
- Identify improvement opportunities, drive cross functional alignment to re\-engineer workflows and implement AI tools and automation solutions using Python/RPA pipelines that streamline manual data aggregation, report generation and analysis across regions.
- Drive change management to embed these solutions into our day\-to\-day operations and user adoption within the Advisory Services finance organization. This includes developing training programs, how\-to guides, and FAQs for agentic tools.
- Ensure consistency for business planning, resource allocation and performance tracking for Advisory Services AI tools across regions, with aligned metrics and reporting standards.
- Conduct data analysis on financial data to assess revenue/cost benefits realization and ROI for AI initiatives and derive actionable insights that inform business strategy and decision\-making.
- Collaborate with global and regional Advisory FP\&A partners, Finance Transformation office and Tech to identify and deliver innovative AI\-first solutions for Advisory Services processes, tools and data architecture integration with enterprise platforms (e.g. Oracle/Hyperion planning, ERP and project tools).
- Serve as the liaison between Advisory Services Finance team and Finance Transformation office and Technology teams on AI initiatives to coordinate tool development and integration with enterprise platforms (e.g. Oracle/Hyperion planning, ERP, project tools and data lakes).
This is a Hybrid position located in our San Francisco office location.
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
- Degree or higher in Finance, Economics, Computer Science or a related field
- 10 years of experience, specifically working in the data science field and demonstrated track record of change management leveraging new technologies
- Demonstrated experience with business processes, implementing analytics or automation projects
- Proficient in visualization/BI tools (e.g., Tableau, PowerBI, Power Automate, Power Apps)
- Effective communication with a wide range of cross\-functional and internal audiences, with experience distilling complex concepts and analysis into concise actionable conclusions
- Attention to detail with an understanding of the big picture \- experience creating a vision with curiosity \& creativity and ability to influence others to align to that vision
- Excellent project management and organizational skills with demonstrated ability in driving change in an organization from concept through to execution
- Practical, hands\-on experience with the AI stacks (OpenAI, Gemini, Anthropic, Microsoft), and experience managing vendor/consulting relationships, forecasting technical costs (tokens), and monitoring system uptime/SLAs impact to agentic workflows efficacy.
- “Hands\-on\-keyboard" mentality, taking an idea from stakeholders and turn it into a working agentic workflow without needing external engineering resources
- Adaptable to change and comfortable working in a fast\-paced and ambiguous environment, continuously evolving with emerging trends in AI
- Demonstrated ability to integrate AI tools to optimize/redesign workflows and drive measurable impact in key processes and deliverables (e.g., efficiency gains, quality improvements)
- Understanding of enterprise reporting systems, e.g. Oracle Hyperion Planning, SAP or similar EPMs, Microsoft project suites or other integrated ERPs
- Proficiency in data analysis and automation tools with hands\-on ability to work with Python and SQL to manipulate data and automate tasks
- Exposure to AI APIs, MCP and NLP tools (such as GPT\-based services)
- In\-depth knowledge of machine learning models and using Python libraries (pandas, scikit\-learn or similar).
U.S. Applicants Only
The estimated salary range for this position is $170,700\.00 to $ 273,200\.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, including the requirements of Article 49 of the San Francisco Police Code.
Salary Context
This $170K-$273K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $181K across 1996 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 3,824 AI roles we're tracking, AI/ML Engineer positions make up 71% 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 Required
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 $178,940 based on 11,900 positions with disclosed compensation. Director-level AI roles across all categories have a median of $243,000. This role's midpoint ($221K) sits 24% above the category median. Disclosed range: $170K to $273K.
Across all AI roles, the market median is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($293,500) and AI Safety ($274,200). By seniority level: Entry: $97,380; Mid: $160,000; Senior: $227,400; Director: $243,000; VP: $250,000.
Visa AI Hiring
Visa has 8 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer. Positions span Highlands Ranch, CO, US, San Francisco, CA, US, Foster City, CA, US. Compensation range: $198K - $400K.
Location Context
AI roles in San Francisco pay a median of $253,000 across 1,990 tracked positions. That's 26% above the national 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 3,824 open positions tracked in our dataset. By seniority: 119 entry-level, 1,813 mid-level, 1,472 senior, and 420 leadership roles (Director, VP, C-Level). Remote roles make up 16% of the market (613 positions). The remaining 3,187 roles require on-site or hybrid attendance.
The market median for AI roles is $200,000. Top-quartile compensation starts at $253,000. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($293,500 median, 31 roles); AI Safety ($274,200 median, 51 roles); Research Engineer ($260,000 median, 401 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 3,824 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,702), Data Scientist (281), AI Software Engineer (258). 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 (119) are outnumbered by mid-level (1,813) and senior (1,472) 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 420 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 16% of all AI roles (613 positions), with 3,187 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 $200,000. Top-quartile roles start at $253,000, 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 $293,500 median, while Prompt Engineer roles sit at $142,800. 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 (1,968 postings), Aws (1,203 postings), Azure (882 postings), Rag (877 postings), Gcp (735 postings), Prompt Engineering (587 postings), Pytorch (586 postings), Claude (554 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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