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 Mastercard's Business & Market Insights (B&MI) group empowers organizations to achieve growth and innovation goals by delivering unparalleled data-driven intelligence and cutting-edge AI solutions. By harnessing proprietary data, frontier generative AI, and global expertise, B&MI helps businesses make smarter, faster, and more impactful decisions. We transform complex, multi-modal data into agentic systems and generative applications that drive measurable business outcomes and sustained competitive advantage. We're looking for a Senior AI Engineer, Generative AI & ML Engineering, for the Operational Intelligence Program within B&MI. In this hands-on role, you'll design, build, and ship production-grade agentic AI systems. These include agents that make extensive tool calls into enterprise services and APIs, handle complex multi-step workflows, and deliver secure, scalable, reliable results. You'll work across agent orchestration, tool calling, memory, retrieval, and LLMOps, help set the engineering standard for Gen AI development, and mentor other engineers on the team. Much of this work is strong backend engineering. If you bring deep experience building production services and have started building with LLMs, we'd love to Roles & Responsibilities • Architect and build the tool-calling layer that powers our agents: wrap internal services, data platforms, and external APIs as well-defined tools with clear schemas, contracts, and permissions. • Engineer reliable tool execution at scale, including parallel and chained tool calls, timeouts, retries, idempotency, error recovery, and graceful handling of tool failures or bad model outputs. • Develop high-performance Python backend services for LLM orchestration, covering async job handling, streaming responses, caching, rate limiting, and distributed workflows supporting high-throughput Gen AI operations. • Engineer state, memory, and context management subsystems so agents can maintain session continuity across long, multi-step, tool-heavy interactions. • Architect multi-agent and skills-based agent systems using frameworks such as LangGraph, CrewAI, AutoGen, or similar, enabling autonomous reasoning, tool use, inter-agent coordination, and adaptive decision-making at enterprise scale. • Build observability for agent behavior, including end-to-end tracing of tool calls, measuring tool-selection accuracy, and debugging multi-step agent runs in production. • Build retrieval systems (RAG and Graph-RAG) where agents need grounded knowledge, using vector databases (e.g., Pinecone, pgvector, OpenSearch) and knowledge graphs (e.g., Neo4j, AWS Neptune). • Establish LLMOps and MLOps pipelines on Databricks (AWS), covering MLflow, prompt and agent evaluation, model lineage tracking, CI/CD, and monitoring. • Design multimodal generative AI pipelines that unify text, image, tabular, and graph data using modern transformer-based and foundation models. • Guide fine-tuning, prompt engineering, and alignment strategies (e.g., PEFT, LoRA, instruction tuning, RLHF) to adapt foundation models for specialized enterprise use cases, working closely with ML specialists. • Implement Responsible AI practices, including hallucination mitigation, bias detection, output sa