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 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 realise 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 CNPF Data & AI organisation is looking for a Senior AI Engineer to contribute hands-on to the delivery of applied AI and agentic capabilities across our platforms. This role sits at the intersection of software engineering, machine learning engineering, and applied data science, with a strong focus on building and operating production-grade AI systems. This is a senior individual contributor role. You will work closely with Applied AI, Data Science, and Product teams to help take AI solutions from experimentation through to secure, scalable production - bringing strong engineering rigour and a collaborative mindset to everything you build. Role Develop and contribute to AI and agentic systems across the full lifecycle from design through production deployment Build and operate ML/AI services, pipelines and APIs using strong software engineering practices Implement ML engineering capabilities including model serving, monitoring, evaluation and retraining Partner with data scientists to productionise models and experiments efficiently Contribute to data preparation, feature engineering, experimentation and modelling as needed Participate in technical design reviews and support knowledge sharing across engineering and data science teams Ensure AI solutions meet Mastercard standards for performance, reliability, security and governance Collaborate with platform, security, and infrastructure teams to ship responsibly at scale All about you Solid experience as a hands-on AI engineer, ML engineer, or software engineer working on production AI systems Strong foundations in software engineering, system design, and distributed systems Practical experience productionising machine learning models and supporting their operation at scale Comfortable working across data engineering, ML engineering, and applied data science tasks Familiarity with large-scale data platforms and modern ML/AI tooling Good problem-solving skills with the ability to navigate ambiguous requirements Collaborative and communicative, able to work effectively across functions and disciplines What Makes You Stand Out You have contributed to AI or agentic applications running in real production environments Hands-on experience with agent-based or LLM-powered systems beyond simple POCs Good instincts for reliability, observability, and failure handling in AI systems Ability to move between engineering execution and applied modelling depending on what the problem needs Eagerness to grow technically and contribute positively to the engineering culture around you Corporate Security Responsibility Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organisation and therefore it is expected that the successful candidate 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• Complete all mandatory security trainings in accordance with Mastercard's guidelines 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.

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 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 realise 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 CNPF Data & AI organisation is looking for a Senior AI Engineer to contribute hands-on to the delivery of applied AI and agentic capabilities across our platforms. This role sits at the intersection of software engineering, machine learning engineering, and applied data science, with a strong focus on building and operating production-grade AI systems. This is a senior individual contributor role. You will work closely with Applied AI, Data Science, and Product teams to help take AI solutions from experimentation through to secure, scalable production - bringing strong engineering rigour and a collaborative mindset to everything you build. Role Develop and contribute to AI and agentic systems across the full lifecycle from design through production deployment Build and operate ML/AI services, pipelines and APIs using strong software engineering practices Implement ML engineering capabilities including model serving, monitoring, evaluation and retraining Partner with data scientists to productionise models and experiments efficiently Contribute to data preparation, feature engineering, experimentation and modelling as needed Participate in technical design reviews and support knowledge sharing across engineering and data science teams Ensure AI solutions meet Mastercard standards for performance, reliability, security and governance Collaborate with platform, security, and infrastructure teams to ship responsibly at scale All about you Solid experience as a hands-on AI engineer, ML engineer, or software engineer working on production AI systems Strong foundations in software engineering, system design, and distributed systems Practical experience productionising machine learning models and supporting their operation at scale Comfortable working across data engineering, ML engineering, and applied data science tasks Familiarity with large-scale data platforms and modern ML/AI tooling Good problem-solving skills with the ability to navigate ambiguous requirements Collaborative and communicative, able to work effectively across functions and disciplines What Makes You Stand Out You have contributed to AI or agentic applications running in real production environments Hands-on experience with agent-based or LLM-powered systems beyond simple POCs Good instincts for reliability, observability, and failure handling in AI systems Ability to move between engineering execution and applied modelling depending on what the problem needs Eagerness to grow technically and contribute positively to the engineering culture around you Corporate Security Responsibility Every person working for, or on behalf of, Mastercard is responsible for information security. All activities involving access to Mastercard assets, information, and networks come with an inherent risk to the organisation and therefore it is expected that the successful candidate 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• Complete all mandatory security trainings in accordance with Mastercard's guidelines 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.

• Build foundation models and generative AI tools alongside a team of technologists. • Design and build agentic workflows — multi-agent orchestration (e.g., CrewAI, LangGraph, AutoGen), tool use, multi-step planning, and human-in-the-loop checkpoints — to automate complex engineering tasks. • Establish evaluation, guardrails, and failure-mode analysis for agent systems to ensure they are safe, reliable, and production-ready. • Develop scalable data pipelines for diverse data sources used in production ML systems, including BIM, CAD, and infrastructure design data. • Work with large-scale, multi-modal datasets — including text and geometric data — to design novel preprocessing, augmentation, analysis, and content understanding. • Transform unstructured infrastructure and design data into representations suitable for machine learning. • Lead cross-functional collaboration with ML Research Scientists and Engineers to align data formats with downstream training and fine-tuning of LLMs. • Apply deduplication, normalization, and validation techniques to ensure high-quality data in production environments. • Architect and optimize pipelines for scalability, reproducibility, and cloud deployment. • Mentor junior engineers and provide technical guidance on complex data challenges. • Drive technical decision-making and influence best practices across the team. • Perform requirements analysis with senior stakeholders, ensuring technical solutions meet both immediate project goals and long-term research objectives. • Communicate findings and technical insights through quantitative analysis, visualizations, and clear documentation. • Contribute to agile workflows, ensuring flexibility and responsiveness to evolving project needs. • Participate in technical planning and roadmap development.

• Lead agentic AI development, including multi-agent orchestration patterns, agent-to-agent protocols, and reliable tool use at production scale • Own prompt engineering and evaluation workflows including structured outputs, hallucination reduction, and behavioral consistency • Build and own MCP server infrastructure that exposes backup data to AI agents via the Model Context Protocol, enforcing tenant-aware RBAC, query constraints, and safe tool boundaries • Define AI quality benchmarks for retrieval relevance, summarization accuracy, and agent reliability, and drive systematic improvements through eval-driven iteration • Champion security and safety in AI systems, including adversarial prompt hardening, jailbreak resistance, data boundary enforcement, and OWASP LLM Top 10 awareness • Tune AI workflows for performance, cost, latency, and observability across billions of documents in global regions • Mentor engineers on the team, raise the technical bar, and contribute to architecture reviews and design decisions