Machine Learning Operations Engineer/Senior

Skillhub Solutions Inc.; Canada; Hybrid; CAD 110,000 - 160,000; Full time

Benefits offered by this job

Annual bonus

Leave top-up

Vacation days

Pension plan

Benefits package

Job summary

Skillhub Solutions Inc. is seeking a Machine Learning Operations Engineer/Senior to design, deploy, monitor and operationalize ML/AI solutions in a hybrid environment across multiple Canadian cities, including Edmonton and Winnipeg.

The role partners with analytics, engineering and architecture teams to scale production ML. The ideal candidate has 5+ years in enterprise ML, hands-on Databricks/MLflow experience, and strong governance and CI/CD practices.

Qualifications

• 5+ years of experience developing and deploying enterprise-scale ML 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.

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.

Skills

MLOps

CI/CD

Generative AI

Cross-functional teamwork

Cloud platforms

Education

Master's degree

Tools

Databricks

MLflow

Unity Catalog

Feature Store

SageMaker

Mosaic AI

Job description

Machine Learning Operations Engineer/Senior

Edmonton, AB Full-time, Part-time CA$110,000 - CA$160,000 / year

Machine Learning Operations Engineer/Senior

Job 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 role

Design, 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.