Senior Machine Learning / MLOps Engineer

N2P Systems; Toronto; On-site; CAD 120,000 - 160,000; Full time

Job summary

N2P Systems is seeking a Senior Machine Learning / MLOps Engineer to join its AI Center of Excellence in Toronto. The role is hands-on and focuses on taking ML prototypes to production, building scalable pipelines, deploying and monitoring models in cloud environments, and supporting the full ML lifecycle.

You will collaborate with data scientists and software engineers to deliver enterprise AI solutions, contribute to standards, and ensure reliable, scalable ML services across the organization.

Qualifications

• 5+ years of hands-on experience in ML engineering, ML platform engineering, MLOps, or a closely related field.
• Strong hands-on experience building and deploying production ML solutions.
• Proficiency in Python and SQL; experience with cloud ML platforms.

Responsibilities

• Design, build, deploy, and operationalize end-to-end machine learning solutions.
• Develop scalable ML pipelines covering data prep, feature engineering, model development, deployment, and monitoring.
• Build and maintain production ML workflows using Azure ML, Databricks, MLflow, MLOps, and CI/CD.
• Take ML models from proof of concept through production deployment.
• Implement model monitoring, lifecycle management, and operational processes for production ML systems.
• Integrate ML solutions with enterprise applications and operational workflows.
• Troubleshoot and provide ongoing production support for ML pipelines and deployed models.
• Partner with data scientists and software/data engineers to accelerate enterprise AI delivery.

Skills

Machine learning engineering

MLOps

Python

SQL

Cloud platforms

Tools

Azure ML

Databricks

MLflow

CI/CD

Job description

We are looking for a Senior Machine Learning / MLOps Engineer to join an enterprise AI Center of Excellence (AI CoE) and help build, deploy, and operationalize production-grade machine learning solutions.

This is a hands‑on engineering role for someone who can take ML solutions from prototype to production, build scalable pipelines, deploy and monitor models in cloud environments, and support the complete ML lifecycle.

You will work closely with data scientists and engineering teams to turn AI/ML initiatives into reliable, scalable enterprise solutions.

What You'll Do

• Design, build, deploy, and operationalize end-to-end machine learning solutions.
• Develop scalable ML pipelines covering data preparation, feature engineering, model development, deployment, and monitoring.
• Build and maintain production ML workflows using Azure ML, Databricks, MLflow, MLOps, and CI/CD.
• Take machine learning models from proof of concept through production deployment.
• Implement model monitoring, lifecycle management, and operational processes for production ML systems.
• Integrate ML solutions with enterprise applications and operational workflows.
• Troubleshoot and provide ongoing production support for ML pipelines and deployed models.
• Partner with data scientists and software/data engineers to accelerate enterprise AI delivery.
• Contribute to engineering standards and best practices for scalable and maintainable ML solutions.

What We're Looking For

• 5+ years of hands‑on experience in machine learning engineering, ML platform engineering, MLOps, or a closely related field.
• Strong hands‑on experience building and deploying production machine learning solutions.
• Expert‑level proficiency in Python and SQL.
• Deep experience with Azure ML, Databricks, and MLflow.
• Strong understanding of MLOps and CI/CD pipelines.
• Practical experience with model deployment, monitoring, and lifecycle management.
• Proven experience taking ML models from prototype to production.
• Experience developing scalable ML pipelines and production‑grade ML services.
• Experience working with cloud‑based ML platforms and enterprise data/AI environments.
• Ability to work independently and collaborate effectively with data scientists and engineers.

Nice to Have

• Experience with Generative AI and LLM applications.
• Experience with agentic AI frameworks.
• Knowledge of GenAIOps, including evaluation, monitoring, deployment, and lifecycle management of GenAI solutions.
• Experience supporting enterprise AI/ML platforms in production.