Lead Engineer, Machine Learning Engineering

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

Lead Engineer, Machine Learning EngineeringMastercard's Business & Market Insights (B&MI) group empowers organizations to achieve growth & innovation goals by providing unparalleled data-driven insights and advanced analytics. By leveraging proprietary data and global expertise, B&MI helps businesses make smarter, more informed decisions that drive profitability and success. We turn complex data into actionable strategies that lead to better outcomes and sustained competitive advantage.

We are currently looking for a 'Lead Engineer, Machine Learning Engineering' for Operational Intelligence Program, within B&MI group. This role will lead ML engineering team to execute on AI/ML strategy for the program that enables business growth, enhances customer experience, and ensures delivery of secure, scalable, and high-performing software solutions. As a technology leader, this role will also focus on engineering best practices, next gen innovation and stakeholder management, while fostering a culture of continuous learning and technical excellence within the team.

Roles and Responsibilities:

• Architect and implement multi-agent frameworks (LangGraph, CrewAI, AutoGen) enabling autonomous orchestration, reasoning, and collaboration across specialized agents.

• Design scalable AI pipelines integrating LLMs, vector databases, and Graph-RAG architectures leveraging knowledge graphs (Neo4j, AWS Neptune) for structured, context-rich reasoning.

• Develop modular APIs and SDKs for reusable agent components, persistent memory, and dynamic context propagation.

• Leverage advanced Python development for backend orchestration, async task execution, and high-throughput data workflows.

• Implement robust state and memory management to ensure persistent context, temporal continuity, and inter-agent knowledge sharing.

• Deploy and manage distributed microservices using Kubernetes, Docker, and CI/CD workflows for reliability, scale, and maintainability.

• Establish LLMOps pipelines on Databricks (AWS) integrating MLflow, feature stores, model lineage, and prompt governance for continuous improvement of LLM-based agents.

• Apply traditional AI/ML techniques and statistical methods (regression, clustering, time-series, ensemble models) to augment LLM reasoning with grounded predictive insights.

• Implement observability, monitoring, and governance frameworks ensuring explainability, policy compliance, and model performance transparency.

• Continuously research, benchmark, and productionize advances in LLMs, RAG, and agentic orchestration to drive enterprise-scale intelligence and automation.

All About You:

• Master's/bachelor's degree in computer science or engineering, and a considerable work experience with a proven track-record of successfully leading and managing complex projects/products and delivering to aggressive market needs.

• Expert-level hands on experience designing, building and deploying both conventional AI/ML solutions and LLM/Agentic solutions.

• Strong analytical and problem-solving abilities, with quick adaptation to new technologies, methodologies, and systems.

• Strong applied knowledge and hands on experience in advanced statistical techniques, predictive modelling, machine learning algorithms, GenAI and deep learning frameworks. Experience with AI and machine learning platforms such as TensorFlow, PyTorch, or similar.

• Strong programming skills in languages such as Python/SQL is a must. Experience with data visualization tools (e.g., Tableau, Power BI) and understanding of cloud computing services (AWS, Azure, GCP) related to data processing and storage is a plus.

• Responsible AI knowledge: Awareness of the principles and practices surrounding responsible AI, including fairness, transparency, accountability, and ethics in AI deployments.

• High-energy, detail-oriented and proactive, with ability to function under pressure in an independent environment, along with a high degree of initiative and self-motivation to drive results.

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.

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