Job Description We are seeking a Staff MLOps Engineer with experience building and scaling infrastructure for large 2D and 3D media datasets.

In this role, you will develop and own the backbone of our machine learning lifecycle, ensuring that data pipelines are automated, reproducible, and highly performant at scale.

You will work on enabling seamless model training, deployment, and monitoring across complex, multimodal systems, supporting the evolution of cutting-edge AI/ML applications.

Nous sommes la recherche dun(e) ingnieur(e) MLOps expert(e) ayant de lexprience dans la conception et la mise lchelle dinfrastructures pour de grands ensembles de donnes multimdias 2D et 3D.

Dans ce rle, vous dvelopperez et serez responsable des fondements du cycle de vie de lapprentissage automatique, en veillant ce que les pipelines de donnes soient automatiss, reproductibles et performants grande chelle.

Vous contribuerez lentranement, au dploiement et au suivi des modles au sein de systmes multimodaux complexes, soutenant ainsi le dveloppement dapplications dIA/AA de pointe.

Key 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.

Responsabilits principales Collaboration interfonctionnelle: Collaborer avec les ingnieurs ML, les quipes dannotation et les TPM afin de dfinir les besoins en infrastructure et en entranement.

Automatisation des pipelines: Concevoir, dployer et maintenir des pipelines CI/CD et dentranement continu (CT) pour des systmes dapprentissage automatique multimodaux.

Gestion du cycle de vie des donnes:



Mettre en place des stratgies de stockage et de versionnement pour des ensembles de donnes 2D/3D grande chelle afin dassurer la reproductibilit et un accs efficace.

Surveillance et observabilit: Dvelopper et grer des systmes permettant de surveiller la performance des modles, dtecter la drive des donnes et garantir la fiabilit en production.

Qualifications Master''s degree in Computer Science, Engineering, Mathematics, or a related field Minimum of 5+ years of relevant industry experience, ideally within a fast-paced, high-growth tech environment.

Professional Experience: Proven experience as an MLOps Engineer in a fast-paced environment in applied machine learning.

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

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

Containerization & Orchestration: Deep familiarity with Docker, Kubernetes, and workflow orchestrators (, Airflow, Prefect, or Kubeflow) Ecosystem: Familiarity with cooperative tools such as Jira/Confluence, Slack and a Git server.

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

Attributes: Conscientiousness: High attention to detail regarding system reliability and data security.

Systems Thinking: Ability to translate abstract ML requirements into concrete, scalable cloud or on-prem infrastructure Matrise en informatique, en ingnierie, en mathmatiques ou dans un domaine connexe.

Minimum de 5 ans dexprience pertinente en industrie, idalement dans un environnement technologique dynamique et en forte croissance.





Exprience dmontre en tant quingnieur(e) MLOps dans des environnements dapprentissage automatique appliqu.

Une exprience dans des secteurs multidisciplinaires tels que la robotique, les rseaux intelligents, lagriculture de prcision, les jeux vido ou larospatiale est fortement valorise.

Comptences techniques Outils principaux: Excellente matrise de Python, Git et des environnements Unix.

Conteneurisation et orchestration: Expertise approfondie avec Docker, Kubernetes et des outils dorchestration de workflows (ex. : Airflow, Prefect, Kubeflow). cosystme: Familiarit avec des outils tels que Jira, Confluence, Slack et les workflows collaboratifs bass sur Git.

Bases mathmatiques (atout) : Comprhension des concepts mathmatiques lis au traitement de donnes 3D grande chelle et loptimisation des systmes.

Qualits recherches Rigueur: Grande attention aux dtails, avec un accent sur la fiabilit des systmes, lvolutivit et la scurit des donnes.

Pense systmique: Capacit traduire des besoins ML abstraits en solutions dinfrastructure concrtes et volutives (cloud ou sur site).

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 AccessibilityS.

📌 Staff MLOps Engineer - Ingénieur(e) MLOps expert(e) (niveau Staff)
🏢 NBCUniversal
📍 Montreal