Machine Learning Engineer

BRP•Montreal, QC

53d

About The Position

Be a part of a fast-paced culture and community in a startup-like environment inside one of the most successful organizations in Québec, Canada. We are focused on building a highly talented and ambitious team from the ground up that revolutionizes the way we work around Data & Analytics at BRP. We are a team that has the potential to unlock tremendous value for the organization and you have the unique opportunity to be part of it from the beginning. We are looking for a talented Machine Learning Engineer to join our team. You will play a crucial role in standardizing and deploying machine learning/advanced analytics projects, while also contributing to the continuous improvement of our platform (DevOps/MLOps). This position offers a unique opportunity to work at the intersection of machine learning, data engineering, and DevOps, helping to shape the way we deliver scalable and efficient AI-driven solutions at BRP.

Requirements

3+ years of experience working as a Machine Learning Engineer or Data Engineer with a focus on machine learning projects.
Proficiency in Python and SQL for machine learning, data processing, and automation.
Solid understanding of software engineering best practices (e.g., version control, modularity, testing, and CI/CD).
Intermediate knowledge of classic machine learning algorithms and model evaluation techniques.
Basic understanding of DevOps/MLOps concepts, including CI/CD, and monitoring.
Ability to work independently and take ownership of tasks.
Excellent verbal and written communication skills.
Fluency in English

Nice To Haves

Experience with Dataiku for managing machine learning workflows.
Familiarity with Snowflake as a cloud-based data warehouse.
Knowledge of Dagster for orchestrating data pipelines.
Exposure to Azure DevOps for CI/CD, automation, and infrastructure management.
Knowledge of French

Responsibilities

Design, develop, and deploy machine learning and advanced analytics models in production environments.
Standardize ML workflows to ensure scalability, reproducibility, and maintainability.
Collaborate with data scientists and engineers to improve data pipelines and optimize model performance.
Implement and maintain MLOps/DevOps best practices to streamline model training, testing, and deployment.
Contribute to platform improvements by automating processes, monitoring system performance, and implementing CI/CD pipelines.