Senior AI Engineer

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

Overview:
Mastercard Foundry is a global innovation group focused on the evolution of technology and consumer trends and how these changes impact the payments and commerce industry. Foundry is a collective of interdisciplinary teams powering Mastercard's ability to create and deliver differentiated products, services, and experiences.

Within Foundry, the global Research and Development (R&D) team conducts applied research on emerging technologies to create innovative products for Mastercard. Foundry R&D is made up of talented, passionate developers, engineers, scientists, designers, researchers, data specialists, and program managers. We build knowledge in emerging technologies like agentic AI, generative AI and machine learning to conceptualize and create new products and services, which we then market test with our customers. We also bring the knowledge of cutting and bleeding edge technology to create concepts of the future which not only inspires our customers but also fosters the culture of innovation within Mastercard.
To support this range of projects we are looking for a talented Senior AI Engineer to join the Mastercard Foundry R&D team in our Dublin TechHub.

Role
As a Senior AI Engineer at Mastercard, you will be a key member of our AI delivery team, responsible for designing, building, and scaling AI microservices that transform proof-of-concept (PoC) notebooks into robust, production-ready components. Working closely with data scientists, MLOps engineers, and cross-functional partners, you will enable the seamless integration of AI services into the broader Mastercard ecosystem. This role offers a unique opportunity to bridge engineering and AI best practices in Applied AI.

Key Responsibilities
• AI Microservice Development: Design and implement AI-powered microservices (e.g., ML, agentic AI) with a focus on modularity, scalability, and reusability to support diverse business units and solutions.
• Productionalization:Transition AI models and agentic workflows from notebook-based PoCs to production-grade components, ensuring performance, reliability, and maintainability.
• API Development: Design and implement robust APIs for AI microservices to facilitate seamless integration within the Mastercard ecosystem.
• Quality Assurance: Develop and execute rigorous testing strategies (unit, integration, performance) to ensure the accuracy, stability, and quality of deployed AI services.
• Performance Optimization: Identify and address bottlenecks in AI microservices and infrastructure, optimizing for latency, throughput, and cost efficiency.
• Pipeline Development: Work with data science and infrastructure teams to design reusable workflows (e.g., feature engineering, model optimization, evaluation pipelines).
• Cross-Functional Collaboration: Partner with data scientists, MLOps engineers, product owners, and external stakeholders to translate business requirements into technical specifications.
• Technology Innovation: Research and evaluate emerging technologies (e.g., LLMs, frameworks, methodologies) to enhance AI software development and large-scale data processing capabilities.
• Compliance & Ethics: Ensure all AI microservices adhere to Mastercard’s security standards, compliance policies, and ethical AI principles, while contributing to AI engineering best practices.

Qualifications
• Education: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field. Master’s degree preferred.
• Experience: Minimum of 6+ years in software development, with at least 3 years focused on building and deploying AI/ML powered applications or microservices in production environments.

Technical Skills
• Strong proficiency in Python, Java and relevant frameworks (e.g., Spring Boot, Spring AI, FastAPI, LangChain, LangGraph, Semantic Kernel).
• Extensive experience designing, developing, and deploying RESTful APIs and microservices.
• Proficiency with containerization technologies.
• Proven experience delivering GenAI, Agentic AI workflows in production.
• Proven experience scaling machine learning models from prototype to production, including familiarity with feature stores, model registries, and inference patterns.
• Solid understanding of the AI/ML lifecycle, from data preparation and model training to deployment and monitoring.
• Experience with cloud platforms and their relevant compute, storage, and AI/ML services, cloud certification preferred.
• Good understanding of ML, Deep Learning.
• Good understanding of neural network and transformer model architecture.
• Good experience of CI/CD pipelines for automated testing and deployment of software and AI models.
• Understanding of data governance, data quality, and data security principles relevant to AI/ML applications.
• Excellent communication, interpersonal, and stakeholder management skills, with the ability to effectively articulate complex technical concepts to both technical and non technical audiences.

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.