Machine Learning Operations Engineer/Senior

Machine Learning Operations Engineer/Senior
Edmonton, AB Full-time, Part-time CA$110,000 - CA$160,000 / year
Machine Learning Operations Engineer/SeniorJob ID:
10373
Work arrangement:
Hybrid, with two days per week or 15 hours per week in a Wawanesa office. Work locations include Winnipeg, Calgary, Edmonton, North York, Kitchener, and Ottawa.
Employment:
Full-time and part-time opportunities are indicated.
Salary:
$110,000–$160,000 per year. The offer depends on location, market conditions, pay equity, and the candidate’s skills and experience.
About the roleDesign, develop, deploy, monitor, and operationalize machine learning and AI solutions. Work with business and analytics teams to turn opportunities into scalable production solutions, partnering with engineering and architecture teams on infrastructure, data pipelines, and secure MLOps practices.
Responsibilities
Identify and prioritize AI and machine learning use cases with business stakeholders and analytics teams.
Build and maintain enterprise MLOps frameworks for model training, deployment, monitoring, governance, and lifecycle management.
Develop and support solutions using Databricks, MLflow, Unity Catalog, Feature Store, and cloud-based AI platforms.
Implement model monitoring for data and model drift, performance, explainability, and alerting.
Establish CI/CD and automated deployment pipelines for machine learning and generative AI solutions.
Ensure compliance with model risk management, Responsible AI, data governance, security, and regulatory requirements.
Work with engineering and architecture teams to define AI/ML deployment standards and practices.
Evaluate emerging technologies, including generative AI, LLMOps, Agentic AI, and advanced analytics.
Qualifications
More than five years of experience developing and deploying enterprise-scale machine learning solutions.
Hands-on experience with Databricks, including Workflows, MLflow, Unity Catalog, Model Serving, Feature Store, and Mosaic AI.
Experience implementing end-to-end MLOps on Databricks, AWS SageMaker, or comparable platforms.
Knowledge of model governance, lifecycle management, Responsible AI, and Model Risk Management frameworks.
Experience building CI/CD pipelines and monitoring models for drift, performance degradation, and operational reliability.
Familiarity with generative AI, LLMs, RAG, prompt engineering, and LLMOps.
Experience with cloud data platforms and AWS services, data governance, security, access controls, metadata management, and auditability.
Experience translating business objectives into AI/ML solutions and working across data science, engineering, operations, and architecture teams.
Experience with Databricks on AWS and in insurance, financial services, or another regulated industry is relevant.
An advanced degree in a related field is listed among the qualifications.
Eligible permanent employees may receive an annual bonus plan, leave top-up programs, vacation and personal days, benefits, and a pension plan.
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