Mastercard
From Mastercard's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
We're looking for a Senior AI Engineer, Generative AI & ML Engineering, for the Operational Intelligence Program within B&MI.
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
AI Engineer II
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 safety guardrails, explainability, and compliance with data ethics standards. Evaluate and productionize advances in agentic AI, multimodal models, and frontier foundation models, benchmarking them against enterprise performance and safety requirements. Lead, mentor, and grow a team of engineers, and set the engineering standard for Gen AI development across the program. All About You Bachelor's or Master's degree in Computer Science, AI/ML, or Engineering, or equivalent practical experience. A strong track record of building and operating production backend systems at scale, including distributed services, APIs, async processing, data stores, and observability. Strong API design and integration experience: you know how to build services that other systems, and now LLMs, can call safely and reliably. Excellent Python engineering skills, including async programming, API development (FastAPI or similar), and microservices, plus SQL proficiency. Hands-on experience shipping at least one LLM-powered application to production that uses tool/function calling, such as agents that call APIs, query databases, or trigger workflows. This can be with OpenAI, Anthropic, Gemini, or open-source models. A working understanding of Gen AI engineering patterns, such as prompt design, embeddings, semantic search, and agent memory, or a demonstrated ability to ramp up quickly on new technical domains. Experience with at least one cloud platform (AWS preferred; Azure or GCP welcome). Experience leading or mentoring engineers, plus strong communication and stakeholder management skills. You should be able to translate technical work into business value. Nice to have: experience with Model Context Protocol (MCP) or building tool servers and connectors for LLM agents. Nice to have: experience evaluating agent performance, such as tool-selection accuracy, task completion rates, or multi-step reasoning reliability. Nice to have: exposure to fine-tuning (LoRA, PEFT, RLHF), multimodal models, knowledge graphs, or MLOps tooling like MLflow, Weights & Biases, SageMaker, or Bedrock. Coming from a backend background? We value engineers who take AI from prototype to production as much as those who train the models. If you've built high-throughput systems and started applying those skills to LLM-powered products, we want to hear from you, even if you haven't worked with every tool listed here
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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