AI Engineer 2

Posted Yesterday
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Howth, Dublin, IRL
Hybrid
Mid level
Blockchain • Fintech • Payments • Consulting • Cryptocurrency • Cybersecurity • Quantum Computing
We are a global technology company in the payments industry.
The Role
Build, test, and deploy transformer-based and generative AI models. Support data preparation, feature engineering, training pipelines, model serving, evaluation, monitoring, and integration into applications using MLOps and software engineering best practices.
Summary Generated by Built In
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
AI Engineer 2
Overview
Mastercard is seeking an AI Engineer II to support the development and deployment of AI solutions that power strategic initiatives within the AI & Data organization. This role provides an opportunity to work on advanced AI technologies, including transformer-based and generative AI models, while contributing to the delivery of scalable, production-ready solutions.
Working closely with Senior, Lead, and Principal AI Engineers, you will help develop, evaluate, and operationalize AI capabilities that address real business challenges. This is a hands-on engineering role focused on building technical expertise, delivering high-quality solutions, and growing into greater levels of ownership and technical leadership.
Role
In this role, you will contribute to the development, testing, and deployment of AI and machine learning solutions across the program.
Key responsibilities include:
Develop and support AI and machine learning models under the guidance of senior team members
Contribute to the development and evaluation of transformer-based and generative AI solutions, including embeddings, tokenization, fine-tuning, and inference workflows
Build and maintain components of data preparation, feature engineering, and model training pipelines
Assist in creating and managing datasets required for model training, validation, and evaluation
Support the implementation and optimization of machine learning workflows and model-serving solutions
Help integrate AI models into applications, APIs, and production environments
Support model testing, validation, monitoring, and performance analysis to ensure quality and reliability
Apply established software engineering and MLOps best practices, including version control, testing, documentation, and deployment automation
Collaborate with AI engineers, data engineers, software engineers, and product teams to implement AI solutions
Troubleshoot issues related to model performance, data quality, and system functionality
Participate in code reviews, technical discussions, and knowledge-sharing activities
Stay current with emerging AI technologies, frameworks, and engineering best practices
All About You
2-5 years of experience in AI engineering, machine learning engineering, data science, software engineering, or a related technical field
Strong programming skills in Python
Experience using AI/ML frameworks such as PyTorch, TensorFlow, or similar technologies
Foundational understanding of machine learning, deep learning, and AI engineering concepts
Familiarity with transformer architectures, large language models, tokenization techniques, embeddings, and generative AI technologies
Experience working with data processing, feature engineering, and model training workflows
Familiarity with model evaluation techniques and performance measurement
Exposure to cloud-based development environments such as AWS, Azure, or GCP
Understanding of software engineering fundamentals, including testing, version control, CI/CD principles, and code quality practices
Familiarity with APIs and integrating machine learning models into applications is a plus
Knowledge of data structures, algorithms, and scalable computing concepts
Strong analytical and problem-solving skills
Effective communication and collaboration skills, with the ability to work in a cross-functional team environment
Demonstrated ability to learn new technologies quickly and adapt to evolving technical requirements
Bachelor's degree or equivalent practical experience in Computer Science, Engineering, Data Science, Mathematics, 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.

Skills Required

  • 2-5 years of experience in AI engineering, ML engineering, data science, software engineering, or related field
  • Strong programming skills in Python
  • Experience with AI/ML frameworks such as PyTorch or TensorFlow
  • Foundational understanding of machine learning, deep learning, and AI engineering concepts
  • Familiarity with transformer architectures, large language models, tokenization, embeddings, and generative AI
  • Experience with data processing, feature engineering, and model training workflows
  • Understanding of software engineering fundamentals, including testing, version control, and CI/CD
  • Bachelor's degree or equivalent practical experience in Computer Science, Engineering, Data Science, Mathematics, or related field
  • Familiarity with model evaluation techniques and performance measurement
  • Exposure to cloud-based development environments such as AWS, Azure, or GCP
  • Familiarity with APIs and integrating machine learning models into applications
  • Knowledge of data structures, algorithms, and scalable computing concepts
  • Effective communication and collaboration skills

What the Team is Saying

Jenny
Mastercard

Mastercard Compensation & Benefits Highlights

  • Retirement Support A 10% company retirement match (401k or equivalent) is explicitly highlighted in company materials. This level of employer contribution stands out as a core strength of the package.
  • Leave & Time Off Breadth A global minimum of 16 weeks fully paid new‑parent leave and generous U.S. PTO (vacation, personal days, holidays, sick time, and bereavement) are clearly spelled out. These provisions indicate broad time‑off coverage across life events.
  • Wellbeing & Lifestyle Benefits Hybrid work, a four‑week “work from elsewhere” option, meeting‑free well‑being days, five paid volunteer days, mental‑health resources, and fitness reimbursement/on‑site gyms are emphasized. Together they reflect a holistic approach to flexibility and wellbeing.

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The Company
HQ: Purchase, NY
38,800 Employees
Year Founded: 1966

What We Do

Mastercard powers economies and empowers people in 200+ countries and territories worldwide. Together with our customers, we’re building a resilient 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.

Why Work With Us

We live the Mastercard Way: creating value in the communities we touch, growing together through the opportunities we see, and moving fast to innovate and scale. Our collaborative culture and our passionate people are the key to what we do, driving meaningful change as one team and connecting everyone to priceless possibilities.

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About our Teams

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Hybrid Workspace

Employees engage in a combination of remote and on-site work.

In our ongoing workplace evolution, we’ve introduced hybrid work, Work-From-Elsewhere Weeks and Meeting-Free Days.

Typical time on-site: 3 days a week
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