Senior AI Engineer

SalaryCA$111k – CA$160k

LocationVancouver, Canada

Typefull-time

SenioritySenior

DepartmentAI Engineering

Company size10,000+ people

First seenSep 12, 2026 · 4w ago

Verified live1d ago

At a glanceSummarised by Seekless from the posting.

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

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

Skills

Python

SQL

Git

Jenkins

Maven

Artifactory

Databricks

Spark

LangGraph

LangChain

CrewAI

GitHub Copilot

Claude Code

MLflow

Comet

Weights and Biases

Docker

Kubernetes

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

About the role

Overview:

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.

Requirements

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.

Nice to have

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.

Legal

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 [email protected] 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.

Benefits

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

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