# Senior AI Engineer, Big Data & AI Solutions at Mastercard

Mastercard is hiring a Senior AI Engineer, Big Data & AI Solutions. Level rates it Builds AI ●●●●; you can [apply on Mastercard](https://jobsbylevel.com/go/258170f2-ceb9-489a-b18f-a8d3ca55bf08?utm_medium=ai-agent).

AI Level 4, AI centrality 90 out of 100. O'Fallon, Missouri.

## Details

- Company: [Mastercard](https://jobsbylevel.com/companies/mastercard)
- AI level: AI Level 4 (score 90 out of 100)
- Location: O'Fallon, Missouri
- Posted: August 27, 2026
- Apply: https://jobsbylevel.com/go/258170f2-ceb9-489a-b18f-a8d3ca55bf08?utm_medium=ai-agent
- Free CV review for this job: https://jobsbylevel.com/cv-review?job=senior-ai-engineer-big-data-ai-solutions-at-mastercard-03f434

## Prepare for this job

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## Description

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

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Source: https://jobsbylevel.com/jobs/senior-ai-engineer-big-data-ai-solutions-at-mastercard-03f434

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