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AceStack

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

Toronto, ON, Canada

Job type: Full-time
Apply by: Oct 28, 2026
Posting language: English
Posted: Sep 28, 2026

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Applications open in a new tab.

Original job description

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.

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More about this job

Responsibilities

Design, build, deploy, and operationalize end-to-end machine learning solutions, pipelines, and services in cloud environments, taking models from proof of concept through production. Implement MLOps and CI/CD practices, monitor and support deployed solutions, and collaborate with cross-functional teams to improve enterprise AI standards and production outcomes.

Requirements

Requires 10+ years of experience and strong production machine learning engineering expertise, including expert-level Python and SQL, Azure Machine Learning, Databricks, and MLflow. Candidates should have experience with MLOps, CI/CD, cloud-based ML architectures, and taking models from prototype to production; Generative AI and LLM experience are desirable.

Skills

Python
SQL
Azure Machine Learning
Databricks
MLflow
MLOps
Machine Learning
Feature Engineering
Model Deployment
Model Monitoring
CI/CD
Cloud-Native Architecture
Data Preparation
Generative AI
Large Language Models
Agentic AI

Visa sponsorship

Not detected in the job text

Keywords

Machine Learning
Machine Learning Engineering
Python
SQL
Azure Machine Learning
Databricks
MLflow
MLOps
CI/CD
Cloud Computing
Cloud-Native Architecture
Data Preparation
Feature Engineering
Model Development
Model Validation
Model Deployment
Model Monitoring
ML Pipelines
Production Support
Generative AI
Large Language Models
Agentic AI
GenAIOps
Model Governance
Responsible AI
Enterprise AI

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