Senior Machine Learning Engineer // Toronto, ON // Hybrid : FTE

Role: Senior Machine Learning Engineer

Location: Toronto, ON

Work Model: Hybrid

Experience: 10+

Role Overview

We are seeking an experienced Senior Machine Learning Engineer to support the AI CoE’s Machine Learning and Data Science initiatives. The ideal candidate will have strong hands-on expertise in designing, developing, deploying, and operationalizing end-to-end machine learning solutions in cloud environments.

This is a highly technical and hands-on role focused on taking ML solutions from proof of concept to production, building scalable ML pipelines and services, and supporting models throughout their production lifecycle.

Key Responsibilities

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Design, develop, deploy, and operationalize end-to-end machine learning solutions in cloud environments.

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Perform data preparation, feature engineering, model development, validation, deployment, monitoring, and production support.

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Build scalable and reusable ML pipelines and services for enterprise AI applications.

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Develop and maintain machine learning solutions using Python and SQL.

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Leverage Azure ML, Databricks, and MLflow for model development, experiment tracking, deployment, and lifecycle management.

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Implement MLOps practices, including model versioning, automated deployment, CI/CD, monitoring, and governance.

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Build and maintain automated CI/CD pipelines for machine learning workloads and production deployments.

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Deploy ML models and services using cloud-native architectures and enterprise deployment practices.

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Monitor model performance, data quality, system health, and production workloads; troubleshoot and resolve issues as required.

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Collaborate with Data Scientists, Data Engineers, Software Engineers, and business stakeholders to deliver production-ready AI solutions.

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Help establish and improve standards for ML engineering, deployment, monitoring, and lifecycle management across the AI CoE.

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Support the transition of ML prototypes and proof-of-concepts into scalable, reliable, production-grade solutions.

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Contribute to production support, continuous improvement, and optimization of deployed ML solutions.

Required Skills & Experience

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Strong hands-on experience as a Machine Learning Engineer / ML Engineer delivering production ML solutions.

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Expert-level proficiency in Python and SQL.

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Strong experience with Azure Machine Learning (Azure ML).

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Hands-on experience with Databricks and MLflow.

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Strong understanding of MLOps, ML lifecycle management, model deployment, and monitoring.

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Experience building and managing CI/CD pipelines for machine learning applications.

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Proven experience taking machine learning models from prototype/POC through production deployment.

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Strong understanding of data preparation, feature engineering, model development, validation, deployment, and production support.

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Experience building scalable ML pipelines and cloud-native ML services.

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Strong understanding of cloud-based machine learning architectures and enterprise application integration.

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Ability to work independently while collaborating effectively within cross-functional engineering and data science teams.

Nice to Have

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Experience with Generative AI and Large Language Models (LLMs).

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Hands-on experience with Agentic AI frameworks and LLM-based applications.

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Knowledge of GenAIOps, including evaluation, monitoring, deployment, observability, and lifecycle management of GenAI solutions.

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Experience integrating AI/ML solutions into enterprise applications and operational workflows.

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Experience with model governance, responsible AI, and enterprise AI standards.