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ML Ops Engineer

59 minutes ago

Toronto, Ontario, Canada Rivago infotech Full time

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.

Job Type: Fixed term contract

Contract length: 12 months

Pay: $55.00-$57.00 per hour

Expected hours: 40 per week