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About This Role
Our Purpose
Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re helping build a sustainable economy where everyone can prosper. We support a wide range of digital payments choices, making transactions secure, simple, smart and accessible. Our technology and innovation, partnerships and networks combine to deliver a unique set of products and services that help people, businesses and governments realize their greatest potential.
Title and Summary
Software Engineer II - Full Stack & AI Overview
The Decision Services Technology team within Mastercard's Business & Market Insights organization develops analytics, reporting, and intelligence solutions that help customers investigate transactions, identify fraud patterns, and make informed business decisions.
As part of a rapidly growing product area, the team is investing heavily in AI-powered capabilities that automate manual analysis and improve customer outcomes. Supporting a product with significant growth and a multi-year roadmap, engineers on this team play a key role in developing, enhancing, and scaling innovative solutions across full-stack, cloud, data, and AI technologies.
As a Software Engineer II, you will design, build, test, deploy, and support software solutions while contributing to new product capabilities and ongoing platform enhancements. This role requires strong technical fundamentals, independent execution, and a passion for continuous learning.
What You'll Do
Software Development
- Design, develop, test, deploy, and support full-stack applications and services.
- Translate business and technical requirements into scalable, maintainable solutions.
- Build REST APIs, backend services, and responsive web applications.
- Utilize Java, Spring Boot, frontend frameworks, and Python where appropriate.
- Apply data structures and programming techniques to solve technical challenges.
- Contribute to AI-enabled capabilities and automation initiatives that improve customer outcomes.
Design & Architecture
- Participate in technical design discussions and architectural reviews.
- Apply established design patterns, engineering standards, and development best practices.
- Support cloud-native, distributed, and microservices-based applications.
- Assist with integrating AI and machine learning capabilities into business solutions.
Quality & Secure Engineering
- Develop and maintain unit, functional, and integration tests.
- Apply secure coding practices throughout the software development lifecycle.
- Participate in code reviews to improve software quality and maintainability.
- Support system, integration, and user acceptance testing activities.
- Identify, troubleshoot, and resolve defects.
Performance & Operational Excellence
- Analyze and improve application performance, reliability, and efficiency.
- Support production incidents, root-cause analysis, and issue resolution.
- Contribute to observability, monitoring, CI/CD automation, and operational readiness.
- Continuously improve supportability and operational stability.
Collaboration
- Partner with Product Owners, Engineering, Architecture, and cross-functional stakeholders to deliver business value.
- Participate in Agile planning, estimation, and delivery activities.
- Work effectively within globally distributed teams.
- Demonstrate initiative, accountability, and a continuous learning mindset.
- Remain current on emerging technologies and industry trends.
Required Qualifications
- Bachelor's degree in Computer Science, Engineering, Information Technology, or a related field, or equivalent practical experience.
- 3-6 years developing and supporting enterprise software solutions.
- Proficiency in Java 8+ and Spring Framework, including Spring Boot.
- Demonstrated success building REST APIs and microservices-based solutions.
- Working knowledge of React, Angular, or comparable frontend frameworks.
- Knowledge of Python for automation, data processing, or AI-related use cases.
- Strong understanding of software design principles, object-oriented programming, and distributed systems.
- Ability to create and execute unit, functional, and integration tests.
- Understanding of secure coding standards and code review practices.
- Strong troubleshooting, analytical, and problem-solving capabilities.
- Proficiency in Oracle, SQL Server, or similar relational database technologies, including advanced SQL development.
- Familiarity with CI/CD pipelines and tools such as Jenkins, Maven, Gradle, SonarQube, or similar technologies.
- Exposure to AI-assisted development tools such as GitHub Copilot.
- Understanding of cloud-native architectures and microservices patterns.
- Knowledge of Agile software delivery methodologies.
- Ability to independently execute assigned work following onboarding and knowledge transfer.
Preferred Qualifications
- Familiarity with AWS, Azure, or GCP.
- Knowledge of Docker and Kubernetes.
- Ability to integrate AI/ML models, services, or APIs into business applications.
- Hands-on use of Python for automation, AI, or machine learning initiatives.
- Understanding of observability and monitoring platforms.
- Familiarity with IntelliJ IDEA, Visual Studio Code, Eclipse, STS, or similar development environments.
- Exposure to analytics, reporting, business intelligence, or data-driven products.
- AI model development capabilities.
Success Measures (First 12 Months)
A successful Software Engineer II will:
- Deliver software features from design through production deployment.
- Contribute to AI-powered capabilities that improve efficiency and customer outcomes.
- Build secure, reliable, and maintainable solutions with low defect rates.
- Apply strong engineering fundamentals across development, testing, troubleshooting, and operational support.
- Improve application performance, stability, and operational readiness.
- Collaborate effectively across product and engineering teams.
- Demonstrate growing ownership of assigned services, features, or application components.
- Expand technical expertise across full-stack, cloud, data, and AI technologies.
Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact [email protected] and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.Corporate Security Responsibility
All activities involving access to Mastercard assets, information, and networks comes with an inherent risk to the organization and, therefore, it is expected that every person working for, or on behalf of, Mastercard is responsible for information security and must:
- Abide by Mastercard’s security policies and practices;
- Ensure the confidentiality and integrity of the information being accessed;
- Report any suspected information security violation or breach, and
- Complete all periodic mandatory security trainings in accordance with Mastercard’s guidelines.
In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in the US, the successful candidate will be offered a competitive base salary and may be eligible for an annual bonus or commissions depending on the role. The base salary offered may vary depending on multiple factors, including but not limited to location, job-related knowledge, skills, and experience. Mastercard benefits for full time (and certain part time) employees generally include: insurance (including medical, prescription drug, dental, vision, disability, life insurance); flexible spending account and health savings account; paid leaves (including 16 weeks of new parent leave and up to 20 days of bereavement leave); 80 hours of Paid Sick and Safe Time, 25 days of vacation time and 5 personal days, pro-rated based on date of hire; 10 annual paid U.S. observed holidays; 401k with a best-in-class company match; deferred compensation for eligible roles; fitness reimbursement or on-site fitness facilities; eligibility for tuition reimbursement; and many more. Mastercard benefits for interns generally include: 56 hours of Paid Sick and Safe Time; jury duty leave; and on-site fitness facilities in some locations.
THIS POSTING RELECTS ONE OR MORE CURRENT VACANCIES ON OUR TEAM, AND MASTERCARD INTENDS TO FILL THIS POSITION BY 01/11/2027. If the fill date has passed, Mastercard intends to fill this position as soon as possible. Pay Ranges
O'Fallon, Missouri: $92,000 - $147,000 USD
Salary Context
This $92K-$147K range is in the lower quartile for AI Software Engineer roles in our dataset (median: $192K across 198 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 9,079 AI roles we're tracking, AI Software Engineer positions make up 5% of the market. At Mastercard, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills Required
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $220,000 based on 1,601 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($119K) sits 46% below the category median. Disclosed range: $92K to $147K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Mastercard AI Hiring
Mastercard has 9 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Product Manager, Data Scientist. Positions span Harrison, NY, US, O'Fallon, MO, US, Pune Division, Maharashtra, India. Compensation range: $147K - $407K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 median).
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
AI Hiring Overview
The AI job market has 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 roles).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
The AI Job Market Today
The AI job market spans 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 Safety roles lead at $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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