Senior MLOps Engineer (AdTech)

Fluent

About The Position

Fluent Media is seeking a Senior MLOps Engineer based in Canada to co-own MLOps practices and establish standards for model deployment, serving, scaling, and infrastructure efficiency. Working with ML scientists, Data Engineering, and Product, this engineer will help ensure models support accurate ad selection through reliable production systems at an optimal cost to serve. The role combines hands-on technical leadership with ownership across the machine-learning lifecycle. The engineer will operationalize models in Databricks and AWS, build and optimize automated workflows, design scalable model-serving infrastructure, and strengthen production capabilities through engineering standards, documentation, design and code reviews, and mentoring. The role requires sound judgment across model performance, reliability, scalability, and infrastructure cost. Fluent is a commerce media solutions provider connecting brands with consumers through ad inventory, first-party data, and proprietary machine learning. The company uses its performance-marketing expertise to help partners monetize and advertisers acquire customers.

Requirements

Strong experience with Databricks (preferred) and AWS-based ML workflows.
Proficiency with Python, SQL, PySpark, and related ML and data engineering tools.
Experience with ML lifecycle management tools such as MLflow and Delta Live Tables.
Proven ownership of architecture and operations for real-time, low-latency relevance models and serving endpoints.
Hands-on experience with CI/CD, automated testing, and monitoring systems.
Ability to independently resolve production challenges and collaborate with ML scientists, engineers, and Product.
Experience reviewing pull requests, establishing engineering standards, and mentoring colleagues.

Nice To Haves

Knowledge of data governance, security, and compliance in cloud environments.
Familiarity with model observability and explainability frameworks.
Experience with stream processing or near-real-time pipelines.
Knowledge of digital marketing or mar-tech metrics.

Responsibilities

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.

Benefits

Competitive compensation.
Career and professional growth opportunities.
Health, dental, and vision insurance.
Pre-tax savings plans and transit/parking programs.
401(k) with competitive employer match.
Volunteer and philanthropic activities throughout the year.
Educational and social events.
Fully stocked kitchen, catered breakfast and lunch, and activity-filled office events.
Recreational sports leagues, networking opportunities, and quarterly team outings.
Competitive bonus.