Job Description Role Summary We are seeking an MLOps Engineer with experience building and scaling infrastructure for large 2D and 3D media datasets and training machine learning models on them.

You will be responsible for the ''backbone'' of our machine learning lifecycle, ensuring that our data and training pipelines are automated, reproducible, and performant at scale.

Responsibilities Cross-Functional Coordination: Work with partner ML and Annotation engineers and TPMs to spec out infrastructure and training requirements.

Pipeline Automation: Design and maintain robust CI/CD and CT (Continuous Training) pipelines for complex multimodal models.

Data Lifecycle Management: Implement versioning and storage strategies for massive 2D/3D datasets to ensure reproducibility and high-throughput access.

Monitoring & Observability: Deploy and manage systems for monitoring model performance and data drift in production environments.

Rsum du poste Nous recherchons une personne Ingnieure MLOps possdant de lexprience dans la conception et la mise lchelle dinfrastructures pour de vastes jeux de donnes multimdias 2D et 3D, ainsi que dans lentranement de modles dapprentissage automatique sur ceuxci.

Vous serez responsable de lossature de notre cycle de vie dapprentissage automatique, en veillant ce que nos pipelines de donnes et dentranement soient automatiss, reproductibles et performants grande chelle.

Responsabilits Concevoir larchitecture de bout en bout du cadre CGU: modles de donnes, systmes dexcution et points dextensibilit Concevoir et faire voluer des API qui exposent les systmes de gameplay de manire scuritaire et flexible aux crateurrices Dfinir le modle de script (langage, environnement dexcution) pour le contenu cr par les utilisateurrices Collaborer avec les quipes IA/ML afin de permettre un agent IA capable de gnrer, modifier et raisonner sur du contenu de gameplay Mettre en place des garde-fous: budgets de performance, isolation (sandboxing), scurit et dterminisme Travailler troitement avec les quipes gameplay,



en ligne et outils afin dassurer la cohrence de la plateforme Orienter la direction technique au moyen de prototypes, de documentation et dimplmentations concrtes Encadrer les ingnieures et influencer les normes dingnierie lchelle des quipes Qualifications Graduate degree in Computer Science, Software Engineering, or a related field. 5+ years of experience as an MLOps Engineer in a fast-paced workplace in applied machine learning.

Technical Proficiency: Core Tools: Fluency with Python, Git, and the Unix shell.

Containerization & Orchestration: Deep familiarity with Docker, Kubernetes, and workflow orchestrators (e.g., Airflow, Prefect, or Kubeflow).

Ecosystem: Familiarity with collaborative tools such as Jira/Confluence, Slack and a Git server.

Strong Mathematical Background: Preferred for understanding the resource demands of 3D data transformations.

Desired Characteristics High attention to detail regarding system reliability and data security.

Ability to translate abstract ML requirements into concrete, scalable cloud or on-prem infrastructure.

Prior experience in industries with complex multi-disciplinary teams such as robotics, smart grids, precision agriculture, game development, or aerospace.

Eligibility Requirements Interested candidates must apply to be considered.

Must be legally authorized to work in Canada.

Must be willing to travel for work related business, if necessary Qualifications de base Diplme dtudes suprieures en informatique, en gnie logiciel ou dans un domaine connexe.

Plus de 5 ans dexprience titre dingnieure MLOps dans un environnement dynamique en apprentissage automatique appliqu.

Comptences techniques: Outils de base: Matrise de Python,



de Git et de lenvironnement Unix.

Conteneurisation & orchestration: Solide connaissance de Docker, de Kubernetes et des orchestrateurs de flux de travail (p.ex., Airflow, Prefect ou Kubeflow). cosystme: Connaissance des outils collaboratifs tels que Jira/Confluence, Slack et un serveur Git.

Solide formation mathmatique: Souhaite afin de comprendre les exigences en ressources lies aux transformations de donnes 3D.

Atouts souhaits Grand souci du dtail en matire de fiabilit des systmes et de scurit des donnes.

Capacit traduire des exigences abstraites en apprentissage automatique en infrastructures concrtes et volutives, infonuagiques ou sur site.

Exprience pralable dans des secteurs comportant des quipes multidisciplinaires complexes, tels que la robotique, les rseaux intelligents, lagriculture de prcision, le dveloppement de jeux ou larospatiale.

Exigences dadmissibilit Les personnes intresses doivent soumettre leur candidature afin dtre considres.

Doit tre lgalement autorise travailler au Canada.

Doit tre dispose se dplacer pour des raisons professionnelles, au besoin.

Additional Information As part of our selection process, external candidates may be required to attend an in-person interview with an NBCUniversal employee at one of our locations prior to a hiring decision. NBCUniversal''s policy is to provide equal employment opportunities to all applicants and employees without regard to race, color, religion, creed, gender, gender identity or expression, age, national origin or ancestry, citizenship, disability, sexual orientation, marital status, pregnancy, veteran status, membership in the uniformed services, genetic information, or any other basis protected by applicable law.

If you are a qualified individual with a disability or a disabled veteran and require support throughout the application and/or recruitment process as a result of your disability, you have the right to request a reasonable accommodation.

You can submit your request to .

📌 Staff MLOps Engineer | Ingénieur·e MLOps Staff (Montreal)
🏢 NBCUniversal
📍 Montreal