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Our Purpose Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, were 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, AI Engineering Mastercards 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: Implement multi-agent intelligence frameworks (LangGraph, CrewAI, AutoGen) to enable reasoning, coordination, and adaptive decision-making across specialized AI agents. Design and operationalize multi-modal AI pipelines combining text, image, tabular, and graph data using transformer-based architectures (BERT, CLIP, LLaVA, T5, Whisper, etc.) for unified intelligence. Build scalable RAG and Graph-RAG systems integrating vector stores and knowledge graphs (Neo4j, AWS Neptune) to enable contextual retrieval, semantic linking, and entity-aware reasoning. Develop and productionize transformer-based models for NLP, vision-language understanding, and sequential prediction tasks leveraging Hugging Face, PyTorch, and TensorFlow ecosystems. Implement advanced Python-based backend services for inference orchestration, async job handling, and distributed data workflows supporting high-throughput AI operations. Establish end-to-end LLMOps and MLOps pipelines on Databricks (AWS) integrating MLflow, feature stores, model lineage, prompt evaluation, and continuous retraining frameworks. Apply traditional AI/ML and statistical modeling techniques (regression, clustering, forecasting, ensemble methods) alongside deep learning models for hybrid interpretability and explainability. Engineer state and memory management subsystems that preserve context, track embeddings, and enable agents to reason temporally across multiple modalities and interactions. Implement Responsible AI practicesbias detection, explainability dashboards, data ethics checks, and performance governance ensuring fairness and transparency of deployed models. Continuously research, benchmark, and productionize innovations in multimodal transformers, generative modeling, and agentic orchestration to drive enterprise-scale intelligence and automation. All About You: Masters/bachelors 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. 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 Mastercards security policies and practices; Ensure the confidentiality and integrity of the information being accessed; Report any suspected information security .
Here's how to pick the right one and stand out in your application.
144.883Jobs
31.687IN
81%EN
That number is real. WorkMundi's database shows 144,883 open engineer roles across the world. India has the most with 31,687 jobs, followed by the United States with 30,084. If you just finished reading one job ad and felt paralyzed by choice, you're not alone—but this scale is actually an advantage. It means you can afford to be selective.
Start by geography and language. The majority of engineer ads—117,837 of them—have the job posting text written in English. Use that as one filter, but remember: the ad text language tells you nothing about whether the role actually requires you to speak English day-to-day. Read the job description carefully. Then check which countries have the volume you're targeting. Singapore, Poland, and Australia round out the top five after India and the US.
Next, learn who's hiring. Accenture has posted 2,801 engineer roles. andurilindustries, speechify, and jobgether are also actively recruiting. If you're applying to one of these names, research their hiring patterns and interview style before you apply. That homework pays off.
When you interview, expect the question every engineer hears: 'Tell me about a time you had to debug a problem that wasn't in your job description.' Have a specific story ready—not a general one. Name the tools, the deadline pressure, and what you learned. Hiring managers listen for whether you see problem-solving as part of the role itself, not a favour.