Software Engineer II

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 Software Engineer II AI/ML Data Engieer II Company Overview Mastercard is a global technology company driving an inclusive, digital economy by making transactions secure, simple, smart, and accessible. Our platforms leverage data, AI/ML, and scalable engineering to power solutions for individuals, financial institutions, governments, and businesses worldwide. Role Overview The ML Engineering team leads the design, deployment, and evolution of AI/ML solutions across Mastercard platforms (on‑prem, cloud, and hybrid). We are seeking an AI/ML Data Engieer II with a balanced background in Machine Learning Engineering and Data Engineering, specializing in graph‑based systems. This role focuses on building, operationalizing, and scaling graph‑driven ML solutions, working closely with Data Science, Platform, and Program teams. Key Responsibilities Graph & Data Engineering Design, build, and evolve enterprise‑scale knowledge graphs, including schema design, data ingestion, and graph modeling Develop reliable data pipelines (batch and streaming) to populate and maintain graph data from multiple sources Ensure data quality, consistency, lineage, and performance across graph and upstream/downstream data systems Optimize graph storage, traversal, and query performance for large‑scale production workloads Support integration of graph platforms (e.g., TigerGraph, Neo4j, GraphDB) within broader data ecosystems Troubleshoot, refactor, and modernize existing graph and data engineering codebases ML Engineering & Graph ML Derive value from knowledge graphs using graph inference, node/edge embeddings, and ML‑based techniques Collaborate with Data Scientists to productionize ML models leveraging graph features and embeddings Implement ML pipelines for training, validation, deployment, and serving of graph‑based ML models Enable model lifecycle management, including versioning, monitoring, and performance validation Apply ML fundamentals (bias–variance trade‑off, model selection, evaluation) in production contexts Support deployment of AI/ML solutions across on‑prem, cloud, and hybrid platforms Platform & Engineering Responsibilities Own software delivery at the component level: design, development, testing, deployment, and support Participate in prioritization and design discussions with Product and Business stakeholders Provide platform services and reusable components to other engineering teams across the organization Adopt new programming languages, tools, and architectural patterns as required Mentor peers and less‑experienced engineers, especially in applied ML and graph engineering Required Experience & Skills Core Engineering & ML Strong understanding of machine learning fundamentals, including model families (tree‑based, neural networks, Bayesian models) Exposure to deep learning, including NLP and Transformer‑based models Hands‑on experience with ML frameworks such as TensorFlow, PyTorch, Keras, or Kubeflow Experience applying ML techniques to knowledge graphs, including embeddings and inference Graph & Data Technologies Experience with graph databases and technologies (TigerGraph, Neo4j, Ontotext GraphDB, or similar) Solid data engineering skills: data modeling, pipeline design, and performance optimization Proficiency in Python (and/or Java/Scala) for data and ML workloads Ability to quickly learn new platforms and frameworks Effectiveness & Core Capabilities Strong ability to manage and validate assumpti ... (truncated, view full listing at source)