ML OPS Engineer

Role:

Senior ML Ops Engineer

Location:

Toronto Canada (Hybrid)

Position Overview

We're looking for a Senior MLOps Engineer to architect and build our production ML infrastructure from the ground up. You'll be responsible for designing and implementing a multi-tenant platform that enables our data science team to deploy machine learning models at scale across multiple wastewater utility customers.

This is a foundational role where you'll establish the patterns, practices, and infrastructure that will support dozens of production models serving critical utility operations.

Key Responsibilities

Design and implement multi-tenant ML model serving infrastructure that supports customer isolation, monitoring, and cost allocation.

Build CI/CD pipelines for automated model training, testing, validation, and deployment.

Establish data quality frameworks including validation, drift detection, and monitoring at scale.

Create model versioning, A/B testing, and rollback capabilities for production deployments.

Collaborate closely with data scientists to establish workflows that enable independent model deployment while maintaining quality and consistency.

Implement observability and monitoring systems for model performance, data quality, and infrastructure health.

Design and document architectural patterns and best practices for the ML platform.

Optimize infrastructure costs across multiple customer deployments.

Ensure security, compliance, and data isolation requirements are met in multi-tenant architecture.

Bridge the gap between pilot/proof-of-concept systems and production-ready infrastructure.