Lead AI Engineer

Lead AI Engineer

Our Purpose

• Be part of a Machine Learning Platform engineering team.


• Work with cutting-edge AI, machine learning, and big data platforms and technologies.


• Design complex, scalable, maintainable, and efficient systems.


• Build and maintain data pipelines, machine learning infrastructure, and platform capabilities that support Data Science solutions.


• Automate and optimize Data Science tasks, machine learning workflows, and engineering processes.


• Drive improvements in platform reliability, scalability, performance, observability, and operational excellence.


• Be responsible for the performance and automated testing of your code.


• Provide technical leadership for development tasks and projects.


• Build cross-team collaboration and architecture ownership of the products and platforms supported by the team.


• Independently analyze, propose, and develop solutions for complex technical challenges and issues.


• Participate in technical design reviews, architecture discussions, and technology strategy.


• Communicate closely and effectively with engineering management, Data Scientists, architects, and peers to gather and understand requirements, share project status, and resolve unexpected issues.


• Ensure and enforce adherence to standards and procedures that result in an environment compliant with information security policies.


• Mentor junior and senior colleagues and contribute to engineering excellence across the organization.

• Relevant senior or lead-level experience in software, data, or AI engineering.


• Strong programming skills in Python and experience with Java, Scala, or similar languages.


• Experience working with large-scale distributed data processing platforms or big data query engines, preferably Apache Spark.


• Experience with Databricks is a plus.


• Experience designing, building, and operating large-scale data platforms and processing systems.


• Experience building and supporting machine learning systems in production environments.


• Strong understanding of machine learning concepts, including feature engineering, model training, deployment, and monitoring.


• Experience with cloud platforms and cloud-native architectures (AWS preferred).


• Experience designing complex systems, architectures, and data flows.


• Strong understanding of distributed systems, scalability, reliability, and performance optimisation.


• Experience with CI/CD practices, infrastructure automation, testing, and platform operations.


• Strong understanding of software development lifecycle processes and tools, including Jira and Confluence.


• Proven ability to write clean, maintainable, and well-tested code that follows engineering best practices and coding standards.


• Experience using modern software engineering practices and AI-assisted development tools to improve engineering productivity.


• Mature understanding and technical leadership of object-oriented design, distributed systems, data structures, networking, database design, and software architecture.


• Strong communication skills and the ability to collaborate effectively with engineers, Data Scientists, product teams, and business stakeholders.


• Proven ability to provide technical leadership and mentor other engineers.


• A philosophy of iteration and continuous improvement (Agile, Scrum).

• Collaborative. We do our best work as a team and value openness, support, and constructive feedback.


• Curious. You enjoy learning new technologies and continuously expanding your expertise in AI, machine learning, and platform engineering.


• Innovative. You challenge assumptions and explore new approaches to solving difficult problems.


• Responsible. You care about building secure, reliable, and maintainable systems that operate at scale.


• Hands-on. You enjoy solving technical challenges and leading by example through your own engineering contributions.


• Impact-focused. You care about enabling Data Scientists and engineers to deliver better outcomes for customers.

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.