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All About You

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, Engineering, or a related field; equivalent practical experience considered.
• Hands-on experience building agentic systems in production or near-production settings, including agent frameworks, tool and API integration, multi-step workflows, evaluation, and guardrails.
• Practical experience with Generative AI and LLMs, including prompt design, RAG patterns, and model selection and evaluation trade-offs.
• Experience partnering with data scientists or researchers to productionize proofs of concept and working with engineering partners to deliver them.
• Proficiency in Python and SQL, with solid software engineering fundamentals in testing, version control, packaging, and code review.
• Experience building and consuming APIs and integrating with internal platforms and third-party services.
• Hands-on experience with CI/CD and build tooling such as Git, Jenkins, Maven, and Artifactory.
• Experience deploying models or applications to production and supporting them operationally, including monitoring, versioning, and troubleshooting.
• Working knowledge of machine learning and deep learning techniques and the model lifecycle, including hyperparameter tuning and validation.
• Experience with Databricks, Spark, or comparable distributed data processing environments.
• Experience working in cloud environments.
• Ability to work independently, communicate technical concepts clearly, and collaborate across data science and engineering teams.

Preferred Qualifications

• Experience with agent and orchestration frameworks such as LangGraph, LangChain, CrewAI, or the Model Context Protocol.
• Experience with agent evaluation and observability, including tracing, regression testing, and quality benchmarking.
• Familiarity with AI-assisted development tools such as GitHub Copilot or Claude Code.
• Experience with MLOps tools such as MLflow, Comet, or Weights and Biases.
• Experience with containerization and orchestration technologies such as Docker and Kubernetes.
• Experience building internal developer tooling, SDKs, or self-service platforms adopted by other engineering teams.
• Familiarity with responsible AI practices, data governance, and sensitive data handling.

Mastercard is a merit-based, inclusive, equal opportunity employer that considers applicants without regard to gender, gender identity, sexual orientation, race, ethnicity, disabled or veteran status, or any other characteristic protected by law. We hire the most qualified candidate for the role. In the US or Canada, if you require accommodations or assistance to complete the online application process or during the recruitment process, please contact reasonable_accommodation@mastercard.com and identify the type of accommodation or assistance you are requesting. Do not include any medical or health information in this email. The Reasonable Accommodations team will respond to your email promptly.

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.

In line with Mastercard’s total compensation philosophy and assuming that the job will be performed in Canada, the successful candidate will be offered a competitive pay based on location, experience and other qualifications for the role and may be eligible to participate in a discretionary annual incentive program.

Pay Ranges

Vancouver, Canada: $111,000 - $160,000 CAD