Senior MLOps Engineer — Databricks & AWS

Fluent, LLC; Toronto; Presencial; CAD 130.000 - 140.000; Jornada completa

Ventajas ofrecidas por este puesto de trabajo

Competitive compensation

Career growth opportunities

Health, dental, and vision insurance

Pre-tax savings plans

Transit/parking programs

401K with employer match

Volunteer activities

Educational events

Descripción de la vacante

Fluent, LLC in Canada is seeking a Senior MLOps Engineer to co-own MLOps practices, establishing standards for deployment, serving, scaling and cost-efficient infrastructure. You will work with ML scientists, Data Engineering and Product to ensure reliable production systems for ad selection.

Responsibilities include operationalizing models, building automated pipelines, and leading the ML lifecycle with a focus on latency, reliability and cost.

Formación

• Hands-on experience with Python, SQL and PySpark in ML/data pipelines.
• Strong knowledge of CI/CD, automated testing and monitoring systems.
• Experience deploying and operating ML models in production with low latency.
• Familiarity with Databricks and AWS-based ML workflows.

Responsabilidades

• Partner with ML scientists to operationalize models in Databricks and AWS.
• Build, monitor and optimize automated pipelines for training, testing and deployment.
• Establish standards for model versioning, automated testing, release controls and performance monitoring.
• Design and scale model-serving infrastructure to meet latency, reliability and cost requirements.
• Lead investigations and resolve complex production issues across the ML lifecycle.
• Document repeatable workflows, review designs and code, and mentor colleagues in MLOps best practices.
• Create and maintain dashboards to track model performance, reliability and infrastructure cost.

Conocimientos

Python

SQL

PySpark

CI/CD

Automated testing

Monitoring

Production deployment

Herramientas

Databricks

AWS

MLflow

Delta Live Tables

Descripción del empleo

Fluent, LLC in Canada is seeking a Senior MLOps Engineer to co-own MLOps practices, establishing standards for deployment, serving, scaling and cost-efficient infrastructure. You will work with ML scientists, Data Engineering and Product to ensure reliable production systems for ad selection.

Responsibilities include operationalizing models, building automated pipelines, and leading the ML lifecycle with a focus on latency, reliability and cost.