Senior MLOps Engineer to deploy and maintain analytical and machine learning models using Azure Machine Learning across environments. - 0138018

Our Vancouver client is seeking a Senior MLOps Engineer to deploy and maintain analytical and machine learning models using Azure Machine Learning across environments. - 0138018

Initial contract until Dec 31, 2025, possibilities of extensions. Hybrid: 3 days/week in Richmond, office.

Overview:

You will also be responsible in the adoption of MLOps best practices, including unit test, drift detection and monitoring capabilities, and implementing, optimizing, and maintaining ML models within Azure DevOps, Azure Machine Learning, and related cloud infrastructure.

Must Have:

• 5-7 years of equivalent work experience building, maintaining, and enhancing end-to-end systems as Machine learning operations, platform engineering, and/or DevOps.
• Demonstrated experience leading ML/analytics development projects in Azure environments.
• Strong knowledge of Microsoft Azure ML, MLOps, DevOps, Python, SQL, and ETL processes.
• Ability to build Azure ML pipelines, understand tools used by data scientists, and decompose business requirements into detailed design specifications and code.
• Ability to build frameworks and automation scripts to accelerate development of common coding patterns.
• Experience investigating, diagnosing, and fixing production issues to meet service level agreement targets, independently completing root-cause analysis and impact assessment, and completing post-event preventative actions.
• Bachelor's Degree in Computer Science, Information Systems, Data Science, or a related field, and

Nice to Have:

• Experience with agile methodologies in a machine learning environment, including continuous integration/deployment, MLOps, and model governance initiatives.
• Experience with independent productionization and gate-keeping processes for machine learning models is expected.
• Ability to effectively manage and validate cloud costs and provide value estimation for machine learning initiatives.
• Experience setting up monitoring systems for model accuracy, drift detection, and production failures.