MastercardSenior AI Engineer
Mastercard is hiring a Senior AI Engineer in Vancouver, Canada. It pays CAD 111k-CAD 160k a year and Level rates it ; you can apply on Mastercard.
Level's read on this job
- Level rates this AI engineer role Builds AI ●●●●: the job is building AI systems.
- The posting mentions Claude, Copilot and LangChain among its tools and skills.
- Level classes it as a senior role.
- Mastercard has 967 other open roles on Level.
AI in this role
Build agent-based applications, internal developer tooling, and production-grade AI systems at Mastercard.
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 EngineerOverview:AI Solutions, part of Mastercard’s AI & Data organization, scales AI across the enterprise, moving use cases beyond pilots into trusted, production-grade capabilities embedded in Mastercard’s platforms and products. Centralizing this capability drives speed to scale, operational resilience, consistent delivery standards, and responsible AI by design, in close partnership with the AI Center of Excellence.
This position sits on the Horizontal Enablement team, reporting to the Manager, AI Engineering who heads the Tooling and Agent Development remit. The remit builds the internal tooling and agentic systems that make AI development faster and more repeatable across Mastercard and works alongside data science teams to move their research products into production. As a Senior AI Engineer, you will independently build and ship agent-based applications and developer tooling, and partner with data science counterparts and engineering partners to take proofs of concept from experiment to deployable solution.
About the Role:
• Independently execute key elements of projects within AI Engineering, resolving problems and roadblocks as they arise.
• Design and build agentic systems and internal tooling, including agent orchestration, tool and API integration, memory and context handling, prompt and workflow design, evaluation harnesses, and guardrails for safe and reliable behavior.
• Contribute to the design and development of scalable AI and machine learning systems that address complex business needs, adhering to engineering best practices.
• Work with data science counterparts to understand their research products and translate proofs of concept into deployable solutions, then liaise with engineering partners to carry those solutions through to production.
• Implement models into production, designing scalable training pipelines and deployment frameworks.
• Conduct hyperparameter tuning and validation to meet targeted performance metrics, ensuring robustness and efficiency.
• Monitor model and agent performance, manage versioning, and update solutions to sustain high-quality outputs.
• Ensure the operational stability and scalability of AI systems, adhering to ethical guidelines and contributing to the organization’s AI infrastructure.
• Contribute to solution development for new tools and services and lead smaller projects as an experienced individual contributor with specialized knowledge.
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.
Pay Ranges
Vancouver, Canada: $111,000 - $160,000 CADHow we rate this
Senior AI Engineer at Mastercard rates 90 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.
Builds AI. The job is building AI systems.
- ●●●● Builds AI80 to 100
- ●●●○ Works on AI60 to 79
- ●●○○ Uses AI40 to 59
- ●○○○ Little AI0 to 39
Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.
Prepare for this job
Built from this posting and from similar jobs on Level.
Skills and AI tools this role asks for
Pay
- Typical AI engineer job
- $182k
Use the typical AI engineer figure as a reference when pay comes up. See AI engineer jobs
Questions you could be asked
- How do you structure and test a prompt to get consistent output from a language model?
- How would you design a retrieval step so the model answers from real data instead of guessing?
- How do you monitor a model once it's live, and how do you know it needs retraining?
- How do you think about the risk of an AI system in this kind of role failing silently?
- Tell me about a project where machine learning was part of your work. What did you do?
Adapt your resume
- List these exact terms on your resume: Prompt engineering, RAG, ML Ops, AI Safety, and Machine learning. An applicant tracking system matches the wording, not the idea.
- Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
- Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.
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