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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.
Visa will accept applications for this role until at least 06\-30\-2026Job Description
We are seeking a highly skilled AI developer with deep expertise in Agentic and Generative AI to drive the next wave of intelligent automation and digital transformation. In this role, you will be at the forefront of designing, implementing, and optimizing AI\-powered solutions integrated into the ServiceNow platform. Your primary focus will be on leveraging artificial intelligence, including custom MCP tooling, Agentic scoped applications, and third\-party Large Language Models—to enhance workflows, automate complex processes, and deliver innovative business outcomes.
You will architect and build custom AI solutions and ensure seamless user experiences through intelligent automation. As a thought leader in AI, you will collaborate with stakeholders to identify opportunities for AI adoption, experiment with emerging technologies, and contribute to Visa’s enterprise AI strategy.
In addition to your AI responsibilities, you will develop and configure integrations between ServiceNow and other enterprise applications, manage the full lifecycle of these integrations (from API development and scripting to framework deployment), and ensure robust, secure, and scalable connections.
Roles and responsibilities:
- Designing, developing, and optimizing AI\-powered solutions (built within the ServiceNow platform and/or integrated through external custom solutions), including the creation of custom MCP tooling and Agentic solutions.
- Leveraging AI solutions to integrate and orchestrate third\-party Large Language Models (LLMs) such as OpenAI, Google Gemini, etc, driving intelligent automation and enhanced user experiences.
- Collaborating with stakeholders to identify opportunities for AI adoption, experiment with emerging technologies, and deliver innovative, AI\-driven business outcomes.
- Troubleshooting and resolving issues with ServiceNow configurations and AI components, ensuring seamless operation and reliability.
- Identifying requirement gaps to ensure high\-quality solutions and providing configuration options with clear pros and cons.
- Designing and implementing intuitive, user\-friendly software that enables customer\-led customization and flexibility.
- Collaborating with internal customers and business analysts to clarify requirements and architect effective, AI\-driven solutions.
- Authoring comprehensive technical design and build documentation for all facets of the technical infrastructure.
- Conducting research and analysis of existing systems to provide accurate time estimates and recommendations to project managers.
- Actively participating in project meetings and daily scrums to communicate development status, progress, and technical insights.
This is a hybrid position. Expectation of days in office will be confirmed by your hiring manager.
Visa will accept applications for this role until at least June 30, 2026\.
Visa requires at least 3 days in office, expectations of these days will be confirmed by your Hiring Manager.Qualifications
Basic Qualifications • 5\+ years of relevant work experience with a Bachelor’s Degree or at least 2 years of work experience with an Advanced degree (e.g. Masters, MBA, JD, MD) or 0 years of work experience with a PhD, OR 8\+ years of relevant work experience. Preferred Qualifications • Proven ability to deliver projects on time and within budget by writing high\-quality code, mentoring junior developers, understanding workload and usage requirements, and producing clear technical documentation that enables knowledge transfer and long\-term supportability across global teams. • Experience designing robust, scalable, and moderately complex architectural solutions that integrate across a broader technical ecosystem, while effectively operating as a collaborative member of a globally distributed team across diverse time zones and cultures. • Minimum of 2 years of relevant professional experience including hands\-on work with AI\-powered solutions, with a bachelor’s degree; or at least 2 years of relevant experience with an Advanced Degree (e.g., Master’s, MBA, JD, MD). • In\-depth knowledge of APIs, web services, and standard relational database concepts. • Proven experience with JavaScript, AJAX, JSON, CSS, REST, SOAP, and HTML. • Strong Python skills and hands\-on experience building RESTful or GraphQL APIs. • Demonstrated experience building GenAI solutions, agents, or conversational applications, exhibiting the ability to differentiate reasoning approaches like ReAct and Chain\-of\-Thought (CoT). • Demonstrate ability to design and architect Agentic Systems, applying various agent architectures (symbolic, BDI, LLM\-based) to design intelligent agents tailored for specific tasks and environments. • Implement Core Agent Capabilities. Develop agents that can perceive, reason, plan, act, and learn, utilizing Python and relevant AI libraries and frameworks. • Analyze and Evaluate Agent Behavior. Critically assess agent performance, understand the complexities of multi\-agent systems and human\-agent interaction, and identify risks. • Navigate Ethical Landscapes. Identify, analyze, and address the ethical challenges and safety considerations inherent in developing and deploying autonomous AI systems, applying principles of responsible AI. • Apply Agentic AI to Solve Problems. Conceptualize and build functional AI agents for practical applications, demonstrating the ability to integrate diverse concepts into cohesive business solutions. • Deep understanding of prompt engineering concepts, LLM fine\-tuning, and retrieval\-augmented generation (RAG). • Familiarity with MCP servers or comparable model\-serving/hosting platforms. • Excellent problem\-solving skills, strong communication, and a collaborative attitude. • Strong analytical skills with the ability to extensively analyze and improve business processes and workflows. • Demonstrated expertise in designing, implementing, and optimizing AI\-driven functionalities integrated with ServiceNow, such as custom Agentic AI skills, GenAI integrations, and intelligent automation. • Strong technical background in ServiceNow\-specific development tools and frameworks (UI Policies, UI Macros, UI Pages, Client Scripts, Script Includes, Business Rules, Mid Server Configuration \& Architecture, Import Sets, Transform Maps, Update Sets). • Strong knowledge of the CMDB, data modeling, data strategies, and integrations. • Knowledge of ServiceNow ITSM/ITOM product portfolio including Change Management, Discovery, and Service Mapping. • Relevant certifications such as Certified ServiceNow Admin (CSA), Certified Implementation Specialist (CIS), or Certified Application Developer (CAD) are highly desirable.U.S. Applicants Only
The estimated salary range for this position is $124,300\.00 to $ 198,600\.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.
Salary Context
This $124K-$198K range is below the median 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. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($161K) sits 10% below the category median. Disclosed range: $124K to $198K.
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
Across all AI roles, 16% (613 positions) offer remote work, while 3,187 require on-site attendance. Top AI hiring metros: New York (2,448 roles, $210,000 median); San Francisco (1,990 roles, $253,000 median); Los Angeles (1,686 roles, $189,000 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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