Senior ML Ops Engineer

8 minutes ago

Toronto, Ontario, Canada E-Solutions Full time

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.

Qualifications


• 8+ years of experience in MLOps, DevOps, or ML Infrastructure engineering.


• Proven experience architecting and building ML platforms from scratch (0→1), not just maintaining existing systems.


• Deep understanding of multi-tenant architecture patterns, including data isolation, security, and cost optimization.


• Strong experience with containerization (Docker, Kubernetes) and orchestration for ML workloads.


• Hands-on experience with at least one major cloud platform (AWS, Azure, or GCP) for production ML deployment.


• Experience designing and implementing CI/CD pipelines for ML models.


• Strong knowledge of data quality monitoring, model drift detection, and observability practices.


• Proficiency in Python and infrastructure-as-code tools (Terraform, CloudFormation, etc.).


• Experience working with Python ML Stack: PyTorch, Scikit-learn, NumPy, and Pandas


• Experience working closely with data scientists to enable their productivity and independence.


• Excellent communication skills - able to explain architectural decisions and tradeoffs to both technical and business stakeholders.


• Bonus points for experience in:


• Experience in time-series data, SCADA systems, or edge computing.


• Previous experience scaling ML systems from pilots to hundreds of production deployments.


• Familiarity with water/wastewater utility operations or industrial control systems.

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