Principal Applied AI App Engineer
Mastercard
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
Principal Applied AI App Engineer
Who is Mastercard? Mastercard is a global technology company in the payments industry. Our mission is to connect and power an inclusive, digital economy that benefits everyone, everywhere by making transactions safe, simple, smart, and accessible. Using secure data and networks, partnerships and passion, our innovations and solutions help individuals, financial institutions, governments, and businesses realize their greatest potential.
Our decency quotient, or DQ, drives our culture and everything we do inside and outside of our company. With connections across more than 210 countries and territories, we are building a sustainable world that unlocks priceless possibilities for all.
Overview
The Global Business Solution Center (GBSC) Tech team is looking for a Principal AI Engineer to steer and implement the GBSC’s AI strategy. The ideal candidate is highly motivated, intellectually curious, analytical with a passion for reliability, scalability and automation.
Principal Applied AI App Engineer role:
- Serve as the GBSC’s design lead for AI based solutions within Mastercard’s evolving AI ecosystem
- Design and develop production grade applications that embed machine learning and generative AI capabilities
- Integrate trained models (ML, deep learning, or LLM-based) into APIs, services, workflows, and UIs
- Collaborate with Data Science and ML Engineering teams to operationalize prebuilt models
- Support model versioning, deployment, monitoring, and updates in production environments.
- Implement guardrails for model behavior, data quality, and responsible AI usage
- Ensure AI applications meet enterprise standards for security, privacy, compliance, and ethical AI usage
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