Staff MLOps Engineer – Ingénieur(e) MLOps expert(e)

NBCUniversal

Staff MLOps Engineer responsible for building infrastructure for AI/ML at NBCUniversal. Managing large media datasets, deploying models, and automating data pipelines.

Posted 6/30/2026full-timeRemote • 🇨🇦 CanadaLeadWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and managing machine learning lifecycle processes, including automated data pipelines, CI/CD, and continuous training strategies. Proficient in deploying and monitoring complex multimodal systems while collaborating effectively with cross-functional teams.

Highest-signal resume keywords

MLOps EngineeringPython ProgrammingCI/CD Pipeline DevelopmentDocker and KubernetesData Pipeline Automation

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Machine Learning LifecycleData Pipeline AutomationModel DeploymentVersioning StrategiesMathematical Analysis

Tools & Technologies

GitUnix ShellDockerKubernetesAirflow

Certifications & Qualifications

Master's Degree in Computer ScienceMaster's Degree in EngineeringMaster's Degree in Mathematics

Industry Keywords

Multimodal SystemsContinuous TrainingData Drift Monitoring3D Data TransformationsFast-Paced Tech Environment

Tech Stack

Tools & technologies

AirflowDockerKubernetesPythonUnix

About the role

Key responsibilities & impact

Develop and own the backbone of our machine learning lifecycle, ensuring that data pipelines are automated, reproducible, and highly performant at scale
Work on enabling seamless model training, deployment, and monitoring across complex, multimodal systems, supporting the evolution of cutting-edge AI/ML applications
Collaborate with partner ML and Annotation engineers and TPMs to spec out infrastructure and training requirements
Design and maintain robust CI/CD and CT (Continuous Training) pipelines for complex multimodal models
Implement versioning and storage strategies for massive 2D/3D datasets to ensure reproducibility and high-throughput access
Deploy and manage systems for monitoring model performance and data drift in production environments

Requirements

What you’ll need

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
Proven experience as an MLOps Engineer in a fast-paced environment in applied machine learning
Prior experience in industries with complex multi-disciplinary teams such as robotics, smart grids, precision agriculture, game development, or aerospace
Fluency with Python, Git, and the Unix shell
Deep familiarity with Docker, Kubernetes, and workflow orchestrators (e.g., Airflow, Prefect, or Kubeflow)
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

Benefits

Comp & perks

Health insurance
Equal employment opportunities
Professional development