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., Tiger Graph, 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 Tensor Flow, Py Torch, Keras, or Kubeflow
Experience applying ML techniques to knowledge graphs, including embeddings and inference

Graph & Data Technologies:

Experience with graph databases and technologies (Tiger Graph, 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 assumptions with stakeholders under tight timelines
Capable of navigating complex, matrixed organizations to drive clarity and execution
Deep understanding of system architecture and interdependencies, with proactive risk identification
Ability to decompose complex problems into actionable engineering solutions
High attention to detail and strong ownership mindset
Excellent written and verbal communication skills

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.