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 The Capabilities and Data team within Mastercard Services builds the foundational infrastructure that powers hundreds of customer-facing products. Some of the strongest and most useful capabilities are proprietary large language model ensembles that power the fundamental data operations of our analytics and insights solutions. In this role, you will develop and scale shared AI capabilities that support products and teams across Mastercard. Our LLM-based entity resolution system is already in production, supporting multiple teams across Mastercard. We are now expanding it into an enterprise-level capability and need your help to improve its scalability, performance, and cost efficiency while making it easier to adopt and integrate across products. We are looking for a hands-on AI engineer who combines strong software engineering skills with experience bringing AI solutions into production. You should be comfortable tackling open-ended technical challenges, evaluating alternative approaches, and working independently. Working with platform, product, and operations teams, you will take solutions from design through deployment and ongoing operation. Roles & Responsibilities: Design and own the architecture for deploying and operating AI capabilities at scale, including APIs and MCP servers that enable integration with products, agents, and applications. Develop high-performance Python backend services for LLM inference orchestration, async job handling, streaming responses, and distributed data workflows supporting high-throughput operations. Establish robust LLMOps and MLOps pipelines on Databricks (AWS) using MLflow, feature stores, prompt evaluation frameworks, model lineage tracking, and continuous retraining workflows to ensure reliable AI delivery. Apply traditional ML and statistical modeling (regression, clustering, forecasting, ensemble methods) in hybrid architectures alongside LLMs for interpretable, explainability-first decision systems. Translate R&D outcomes into scalable, reliable production solutions, collaborating with researchers to integrate and validate new models and techniques while optimizing inference latency, throughput, and cost without compromising output quality. Continuously research, evaluate, and productionize advancements in generative modeling, agentic AI, multimodal transformers, and frontier foundation models Lead solutions through security and architecture reviews, production approvals, and deployment. Collaborate with platform, product, and consumer teams to support integration, adoption, and engineering standards. All About You: Master's or Bachelor's degree in Computer Science, AI/ML, or Engineering, with significant hands-on experience leading and delivering complex Gen AI or ML engineering programs in production environments. Expert-level, hands-on experience designing, building, and deploying LLM applications, agentic systems, and RAG pipelines — from prototype to production. Experience building MCP servers, or equivalent depth in building and exposing tool interfaces for LLM and agent consumption, with a clear grasp of how an agent actually consumes what you publish. Deep proficiency with LLM ecosystems: OpenAI, Anthropic, Gemini, Hugging