Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, feature engineering, training workflows, and model serving. Apply MLOps practices, collaborate with product and engineering teams, and support production troubleshooting and performance tuning.
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
Senior AI Engineer - Foundation Models and Transformers-2
Overview
Mastercard is seeking a Senior AI Engineer to design, build, and deploy high-quality AI solutions that support key business and product initiatives. This role is hands-on and delivery-focused, contributing directly to the development of production AI systems while collaborating closely with product, data, and engineering partners.
As a Senior AI Engineer, you will work on well-scoped AI initiatives, applying advanced machine learning and software engineering practices to move models from experimentation into reliable, performant production systems. This role represents a critical technical contributor level, with opportunities to grow toward technical leadership and broader system ownership.
Role
In this role, you will be responsible for building and operationalizing AI solutions under the guidance of Lead and Principal engineers.
Key responsibilities include:
Design, develop, and deploy AI and machine learning models to solve defined business and product problems
Contribute to the development and optimization of transformer-based and generative AI models, including fine-tuning, evaluation, and inference workflows
Build and maintain data pipelines, feature engineering logic, and training workflows in collaboration with data engineering teams
Implement model serving and inference solutions, integrating models into downstream applications and APIs
Apply MLOps best practices, including experiment tracking, versioning, automated testing, monitoring, and model performance evaluation
Participate in code reviews, design discussions, and technical planning to ensure high-quality, maintainable solutions
Collaborate with product managers and stakeholders to translate requirements into technical implementations
Support troubleshooting and performance tuning of models and AI systems in production environments
Continuously improve technical skills and stay current with advances in AI, ML frameworks, and engineering practices
All About You
Solid experience developing machine learning or AI solutions and deploying them into production environments
Strong proficiency in Python and experience with ML frameworks such as PyTorch and/or TensorFlow
Hands-on experience with transformer-based models (e.g. BERT-style encoders, generative models, embeddings, or similar architectures)
Experience working with data pipelines and datasets, including data preparation, feature engineering, and training data management
Familiarity with cloud platforms (AWS, Azure, or GCP) and cloud-based ML tooling
Working knowledge of MLOps practices, including model deployment, monitoring, and lifecycle management
Strong software engineering fundamentals, including version control, testing, and code quality practices
Ability to collaborate effectively within cross-functional teams and follow established architectural and engineering standards
Clear communicator with a growth mindset and interest in progressing toward broader technical ownership
Bachelor's degree or equivalent practical experience in computer science, engineering, data science, or a related field
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:
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
Senior AI Engineer - Foundation Models and Transformers-2
Overview
Mastercard is seeking a Senior AI Engineer to design, build, and deploy high-quality AI solutions that support key business and product initiatives. This role is hands-on and delivery-focused, contributing directly to the development of production AI systems while collaborating closely with product, data, and engineering partners.
As a Senior AI Engineer, you will work on well-scoped AI initiatives, applying advanced machine learning and software engineering practices to move models from experimentation into reliable, performant production systems. This role represents a critical technical contributor level, with opportunities to grow toward technical leadership and broader system ownership.
Role
In this role, you will be responsible for building and operationalizing AI solutions under the guidance of Lead and Principal engineers.
Key responsibilities include:
Design, develop, and deploy AI and machine learning models to solve defined business and product problems
Contribute to the development and optimization of transformer-based and generative AI models, including fine-tuning, evaluation, and inference workflows
Build and maintain data pipelines, feature engineering logic, and training workflows in collaboration with data engineering teams
Implement model serving and inference solutions, integrating models into downstream applications and APIs
Apply MLOps best practices, including experiment tracking, versioning, automated testing, monitoring, and model performance evaluation
Participate in code reviews, design discussions, and technical planning to ensure high-quality, maintainable solutions
Collaborate with product managers and stakeholders to translate requirements into technical implementations
Support troubleshooting and performance tuning of models and AI systems in production environments
Continuously improve technical skills and stay current with advances in AI, ML frameworks, and engineering practices
All About You
Solid experience developing machine learning or AI solutions and deploying them into production environments
Strong proficiency in Python and experience with ML frameworks such as PyTorch and/or TensorFlow
Hands-on experience with transformer-based models (e.g. BERT-style encoders, generative models, embeddings, or similar architectures)
Experience working with data pipelines and datasets, including data preparation, feature engineering, and training data management
Familiarity with cloud platforms (AWS, Azure, or GCP) and cloud-based ML tooling
Working knowledge of MLOps practices, including model deployment, monitoring, and lifecycle management
Strong software engineering fundamentals, including version control, testing, and code quality practices
Ability to collaborate effectively within cross-functional teams and follow established architectural and engineering standards
Clear communicator with a growth mindset and interest in progressing toward broader technical ownership
Bachelor's degree or equivalent practical experience in computer science, engineering, data science, or a related field
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.
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Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, feature engineering, training workflows, model serving, and MLOps (tracking, versioning, testing, monitoring). Collaborate with product, data, and engineering teams, perform code reviews, and troubleshoot and optimize production model performance.
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Design, build, and deploy transformer-based and generative AI models into production. Develop data pipelines, training workflows, model serving and inference, apply MLOps practices, monitor performance, collaborate with product and engineering teams, and support troubleshooting and optimization.
Top Skills:
AWSAzureBertEmbeddingsGCPGenerative ModelsMlopsPythonPyTorchTensorFlowTransformers
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
Design, build, and productionize transformer-based and generative AI models. Develop training workflows, data pipelines, feature engineering, model serving, and inference APIs. Apply MLOps best practices (experiment tracking, versioning, testing, monitoring). Collaborate with product, data, and engineering teams to translate requirements, perform performance tuning, and maintain production model reliability.
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