Senior AI Engineer, Big Data & AI Solutions

Mastercard is hiring a Senior AI Engineer, Big Data & AI Solutions. Level rates it ; you can apply on Mastercard.

Level's read on this job

On Level's scale, this AI engineer role is Builds AI ●●●●: the job is building AI systems.
Tools and skills the posting cites include Claude, Copilot and Databricks.
Level classifies it as a senior role.
Level lists 1,000+ other open roles at Mastercard.

AI in this role

Build and scale big data pipelines, machine learning models, and MLOps infrastructure to protect the global payments ecosystem from fraud.

claudecopilotdatabricksclaude-codeapache-sparkpysparkpythonsqlci-cd

ragml-opsbig-datamachine-learningmlopsfeature-engineeringdata-pipelines

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

Senior AI Engineer, Big Data & AI Solutions

Overview
The Security Solutions Data Science team develops AI and machine learning capabilities that help protect the global payments ecosystem from fraud and cyber threats. Supporting Mastercard’s Safety Net product, the team develops and enhances models that analyze billions of transactions and identify suspicious activity.

As a Senior AI Engineer, you will work at the intersection of big data engineering and AI/ML, helping build and support the data, features, models, and production processes behind fraud detection.

Ideal Candidate Profile
You are a big data and AI/ML engineer who has used Apache Spark in a professional environment and has hands-on experience working within CI/CD-driven engineering environments.

You understand how large-scale data moves from raw inputs through data and feature pipelines into machine learning models, and how those models are deployed, monitored, and improved in production.

Large-scale data → Spark/PySpark → Data & Feature Pipelines → ML models
→ Production Deployment→ CI/CD→Monitoring & Drift Detection → Optimization
& Retraining

Experience with Generative AI, RAG, LLMs, or agentic AI is valuable, but the foundation for this role is large-scale data engineering and production AI/ML.

What You’ll Do
Big Data & Feature Engineering
- Build and enhance large-scale data and feature pipelines using Apache
Spark/PySpark, Python, and SQL.
- Transform high-volume data into reliable features and inputs that support
machine learning models.
Production AI/ML
- Partner with Data Scientists and Engineers to support model development,
evaluation, deployment, and production performance.
- Strengthen model monitoring, observability, and drift detection to identify
changes in model or data performance.
- Support the ongoing optimization and retraining of production models and
their supporting pipelines.
MLOps & AI Engineering
- Apply CI/CD, automated testing, version control, and MLOps practices to
improve reliability and scalability.
- Develop AI-powered solutions, including Generative AI and agentic
approaches, that improve engineering workflows and operational efficiency.

Required Qualifications
This role requires hands-on professional experience with Apache Spark, Python, CI/CD, and familiarity with AI-assisted development tools.

- Master’s degree with 2+ years of relevant experience, or Bachelor’s degree
with 5+ years of relevant experience, in Computer Science, Artificial
Intelligence, Machine Learning, Data Science, Engineering, or a related field;
equivalent practical experience will also be considered.
- Hands-on professional experience with Apache Spark/PySpark in large-scale
or distributed data-processing environments.
- Hands-on experience with CI/CD practices, including automated integration,
testing, or deployment workflows.
- Proficiency with Python and SQL and experience building ETL, data, or feature
engineering pipelines.
- Experience working with large-scale data and technologies such as
Databricks, Airflow, Hadoop, Linux/Unix, or comparable data platforms.
- Understanding of the production machine learning lifecycle, including model
development, deployment, monitoring, evaluation, and optimization.
- Strong problem-solving, communication, and cross-functional collaboration
skills.

Preferred Qualifications
- Experience supporting production AI/ML systems, including model
monitoring, observability, or drift detection.
- Experience in fraud, cybersecurity, payments, or risk management.
- Experience with Generative AI, LLMs, RAG, or agentic AI.
- Familiarity with AI-assisted development tools such as GitHub Copilot or
Claude Code.
- Experience with cloud-based data and AI environments.

Technical Environment
- Apache Spark / PySpark (required)
- Python (required)
- CI/CD (required)
- SQL
- Databricks
- Airflow
- Hadoop
- ETL & Data Pipelines
- Feature Engineering
- MLOps
- Model Monitoring
- GenAI / LLMs / RAG / Agentic AI

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 reasonable_accommodation@mastercard.com 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 11/12/2026. If the fill date has passed, Mastercard intends to fill this position as soon as possible.

Pay Ranges

O'Fallon, Missouri: $115,000 - $184,000 USD

How we rate this

Senior AI Engineer, Big Data & AI Solutions at Mastercard rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior AI Engineer, Big Data & AI Solutions is an open job at Mastercard.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in O'Fallon, Missouri.
The job was posted on August 27, 2026.
Seniority: senior.
Applications are handled by Mastercard. Apply on Mastercard

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

RAGML OpsBig DataMachine learningMLOpsFeature EngineeringData PipelinesClaude

Also common in AI engineer jobs at this level

AI agents AWS Azure Google Cloud PyTorch LangChain

Pay

Typical AI engineer job: $183k

This posting doesn't list pay. Use the typical AI engineer figure when pay comes up. See AI engineer jobs

Questions you could be asked

How would you design a retrieval step so the model answers from real data instead of guessing?
How do you monitor a model once it's live, and how do you know it needs retraining?
Tell me about a project where big data was part of your work. What did you do?
Tell me about a project where machine learning was part of your work. What did you do?
Tell me about a project where mlops was part of your work. What did you do?

Adapt your resume

List these exact terms on your resume: RAG, ML Ops, Big Data, Machine learning, and MLOps. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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