Data Engineer ( AI/ML )
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
Data Engineer ( AI/ML )Overview-Mastercard is a global technology company powering one of the world’s fastest payment networks. Our Data Warehouse enables data-driven insights that help customers solve complex business challenges. In this role, you will contribute to a growing organization, collaborating with skilled engineers to deliver innovative and impactful data solutions.
This role is seeking a Machine Learning / AI Data Engineer to support the development, deployment, and operationalization of data science models at scale. This role combines strong data engineering fundamentals with AI/ML expertise to build scalable data platforms, optimize model pipelines, and enable production-ready AI solutions. The ideal candidate will have hands-on experience with Python, SQL, PySpark, cloud technologies, MLOps, and CI/CD practices.
Role -
- Design, develop, and maintain scalable data pipelines and cloud-based data platforms supporting AI and ML workloads.
- Build, deploy, and optimize machine learning models and AI solutions for enterprise-scale applications.
- Develop robust data processing and feature engineering solutions using Python, SQL, and PySpark.
- Support end-to-end ML lifecycle management, including model deployment, monitoring, automation, and governance.
- Implement and maintain CI/CD pipelines for data and machine learning applications.
- Collaborate with data scientists, engineers, and business stakeholders to operationalize AI/ML solutions.
- Ensure data quality, scalability, reliability, and performance across data and ML platforms.
- 5–6 years of experience in Data Engineering, Machine Learning Engineering, or AI-related roles.
- Strong programming expertise in Python.
- Expert-level SQL skills, including data modeling, query optimization, performance tuning, and complex data transformations.
- Strong hands-on experience with PySpark and distributed data processing.
- Experience developing, deploying, and supporting machine learning models in production environments.
- Solid understanding of AI/ML concepts, including Generative AI, LLMs, RAG architectures, AI agents, prompt engineering, and model lifecycle management.
- Experience building and maintaining scalable ETL/ELT pipelines, data lakes, and cloud-based data platforms.
- Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
- Strong understanding of MLOps, including model deployment, monitoring, versioning, automation, and governance.
- Experience implementing CI/CD pipelines and DevOps best practices.
- Knowledge of containerization, orchestration, and cloud-native architectures.
- Strong analytical, problem-solving, and communication skills.
- Experience working in Agile/Scrum environments.
- Ability to collaborate effectively with data engineers, software engineers, data scientists, and business stakeholders.
- Experience integrating AI/ML capabilities into enterprise data platforms and business applications.
- Understanding of data governance, model governance, security, and responsible AI practices.
Corporate Security Responsibility
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.