Lead 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 Lead AI Engineer Overview We are looking for a talented Lead AI Engineer to work with our Foundry Research and Development team to build innovative products delivered at scale to global markets. Role • Designs, develops, and maintains advanced AI and Machine Learning systems to address specific business challenges. • Implements AI/ML models into production environments, designing scalable training pipelines and deployment frameworks. • Builds and optimizes data ingestion, preprocessing, and feature engineering workflows supporting model training and inference. • Fine-tunes AI/ML models to meet targeted performance metrics, ensuring accuracy, robustness, and efficiency. • Automates workflows for model training, testing, deployment, and updates, following CI/CD best practices. • Monitors model performance, manages versioning, and updates models as needed to sustain high-quality outputs. • Ensures operational stability and scalability of AI systems, adhering to ethical guidelines and contributing to the organization’s AI infrastructure. • Guides and mentors junior team members through on-the-job experiences, reviewing work and fostering a culture of continuous improvement to grow expertise around their discipline. Skills • Expertise in creation of 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 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. • Good hands on experience in Retrieval Augmented Generation (RAG), vector database searching, etc. • Awareness and understanding of LLM usage cost implications and architectural choices. • 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. • Well versed with various Machine Learning, Deep Learning and NLP concepts and models for both supervised and unsupervised learning. • 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 and deploying and managing services / APIs. • Working knowledge of cloud platforms like Azure and using Cloud Native services. • Working knowledge of Databricks is good to have. All About You The ideal candidate for this position should: • Possess at least 7 years of relevant work experience in the field of AI and Data Science. • Loves creating innovative solutions to problems in a collaborative and fast passed environment. • Advanced knowledge and understanding of modern software engineering concepts and methodologies. • Passionate about code quality and coding best practices. • Show initiative and demonstrate ... (truncated, view full listing at source)