MLOps Engineer: Production-Ready ML Systems

SmartRecruiters, Inc.; Montreal (administrative region); Hybrid; CAD 110,000 - 150,000; Full time

Job summary

Ubisoft Montréal is seeking a Machine Learning Operations Software Engineer to build reliable, scalable systems that protect player trust and safety. You will join the Player Data domain within Ubisoft's Data Office, blending applied research with software engineering and focusing on production deployment.

Responsibilities include leading end-to-end projects, developing services and APIs for data/model sharing, and managing large-scale data pipelines and cloud infrastructure.

Qualifications

• Strong software development or data engineering background.
• Experience designing and consuming web service APIs.
• Knowledge of cloud environments and containerized systems (Kubernetes, ArgoCD, AWS, Terraform).
• Ability to connect high level vision with technical details.
• Collaborative communication skills.
• Working knowledge of machine learning, including advanced models.
• Experience deploying predictive models into production.
• Familiarity with large-scale data processing tools or platform operations.

Responsibilities

• Lead end-to-end projects from design to production usage.
• Design, develop, and maintain application services and APIs for data and model sharing.
• Build and operate large-scale data processing pipelines.
• Deploy and manage scalable cloud infrastructures.
• Improve platform quality and reliability.
• Contribute to exploratory data and ML projects.

Skills

Python

Rust

Web APIs

Kubernetes

ArgoCD

AWS

Terraform

ML in production

Tools

Docker

Job description

Ubisoft Montréal is seeking a Machine Learning Operations Software Engineer to build reliable, scalable systems that protect player trust and safety. You will join the Player Data domain within Ubisoft's Data Office, blending applied research with software engineering and focusing on production deployment.

Responsibilities include leading end-to-end projects, developing services and APIs for data/model sharing, and managing large-scale data pipelines and cloud infrastructure.