Principal 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 Principal AI Engineer Overview We are looking for a talented Principal AI Engineer to work with our Foundry Research and Development team to build innovative products delivered at scale to global markets. Role • Architects, designs, develops, and maintains advanced AI and Machine Learning systems to address specific business challenges. • Oversees the deployment of models into production, ensuring scalability, reliability, and compliance with ethical guidelines. • Develops and implements the contract or engagement between the Data Engineering team and the AI Engineering team to ensure that data ingestion, preprocessing, and feature engineering workflows support model training and inference. • Develops and refines the technical strategy for AI/ML model optimization and deployment, ensuring alignment with business objectives and industry best practices. • Undertakes research and prototyping efforts for deploying and managing scalable, maintainable AI/ML models, leveraging cutting-edge industry techniques. • Supports high-impact AI/ML projects, providing technical mentorship and ensuring adherence to quality standards. • Collaborates with product teams and stakeholders to translate business needs into effective AI/ML engineering solutions. • Stays abreast of emerging AI/ML engineering trends and incorporates innovative techniques into Mastercard’s AI ecosystem. • Mentors team members by sharing best practices, innovative techniques, and emerging trends to develop expertise and capabilities around their discipline. Skills • Expertise in architecting and creating Agentic AI applications using frameworks like langgraph, CrewAI, AutoGen, and good knowledge of Agentic AI design patterns and related concepts like Context Management, LLMOps, AgentOps, Guardrails, Agent Validation and Evaluation. • Expertise in Prompt engineering and working with both closed source models and open source models. • Experienced in guiding the team in performing experiments and taking decisions on using RAG vs Few Shot Prompting vs LLM Fine Tuning or hybrid approaches to enhance model context and understanding. • Good Working knowledge of MLOps tools like MLflow. • Good Working Knowledge of LLM Finetuning and related concepts like model quantization and distillation. • Good hands on experience in various types of Retrieval Augmented Generation (RAG) including graph-based RAG, vector database searching and optimizing • Deep awareness and understanding of LLM usage cost implications and architectural choices and experienced in performing ROI simulations considering these aspects. • Experienced in architecting, implementing, and maintaining robust CI/CD pipelines to automate code integration, testing, and deployment, ensuring high velocity and reliability in software delivery. • Proficiency with Python and related ecosystem of Data Science tools and packages including numpy, pandas, sklearn, spacy, keras, torch, transformers, langgraph. • Good hands-on experience in applying various Machine Learning, Deep Learning and NLP concepts and models for both supervised and unsupervised learning to solve real world business problems. • Working knowledge of Python based API based frameworks like FastAPI and comfortable working with JSON objects. • Good working knowledge of pyspark with conceptual understanding of parallel processing for huge data volumes. • Experience working with Unix commands for accessing various systems and databases ... (truncated, view full listing at source)