Senior AI Engineer, Big Data & AI Solutions
$115k - $184k • O'Fallon, Missouri • Full-time
Posted 3d ago
About the job
The Security Solutions Data Science team at Mastercard develops AI and machine learning capabilities to protect the global payments ecosystem from fraud and cyber threats. As a Senior AI Engineer, you will work at the intersection of big data engineering and AI/ML, focusing on building and supporting the data, features, models, and production processes essential for fraud detection. This role involves transforming high-volume data into reliable features for machine learning models and ensuring the robust deployment, monitoring, and improvement of these models in production environments.
Responsibilities
- 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 for machine learning models.
- Support model development, evaluation, deployment, and production performance in partnership with Data Scientists and Engineers.
- 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.
- 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, to improve engineering workflows and operational efficiency.
Requirements
- Master's degree with 2+ years of relevant experience, or Bachelor's degree with 5+ years of relevant experience, in a related field, or equivalent practical experience.
- 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, with 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.
- Experience supporting production AI/ML systems, including model monitoring, observability, or drift detection.
- 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.