Machine Learning Engineer

Altis; Toronto; Híbrido; CAD 156.000 - 246.000; Jornada completa

Descripción de la vacante

Altis is seeking a hands-on Senior Machine Learning Engineer to design, deploy, and monitor ML models and MLOps pipelines using Azure ML and Foundry, bridging experimentation and production.

This six-month hybrid role in Toronto, Canada, offers production-scale opportunities, collaboration with cross-functional teams, and exposure to Azure AI Foundry, CI/CD, containerized deployments, and model observability.

Formación

• Hands-on ML engineering with production deployments.
• Strong Azure ML and Foundry/Azure AI Foundry expertise; CI/CD and MLOps.
• Proficient in Python, SQL; data prep and feature engineering.

Responsabilidades

• Design, develop, deploy, and operationalize ML/ DL models using Azure ML and Foundry.
• Build reusable MLOps pipelines, CI/CD workflows, containers, APIs, and automation for deployments.
• Establish monitoring, drift detection, retraining, and performance optimization.
• Collaborate with stakeholders to translate business goals into scalable AI solutions.
• Support data prep, quality assurance, experimentation, and priority management.

Conocimientos

Azure ML

MLOps

CI/CD

Python

SQL

Databricks

Herramientas

Databricks

Power BI

VS Code

Git

Jira

Descripción del empleo

Senior Machine Learning Engineer

Location: Hybrid, 3 days per week on-site

Language: Strong written and verbal English communication skills

Duration: 6-month contract with the possibility of extension

About the Opportunity

Are you a hands-on Machine Learning Engineer who enjoys turning innovative AI solutions into reliable, production-ready systems? This opportunity offers the chance to help a large enterprise organization scale its AI capabilities by building robust deployment frameworks, automation, and monitoring solutions.

Working alongside technical teams and business stakeholders, you'll play a key role in bridging the gap between machine learning experimentation and production. Your expertise will help establish scalable MLOps practices that improve reliability, performance, and the delivery of AI solutions across the organization.

What's In It for You

• Work on high-impact AI and machine learning initiatives with enterprise-wide reach.
• Collaborate with experienced technical professionals in an innovative, team-oriented environment.
• Gain exposure to modern Azure AI technologies and advanced MLOps practices.
• Enjoy a hybrid work arrangement with the possibility of a contract extension.

Your Responsibilities

• You'll design, develop, deploy, and operationalize machine learning and deep learning models using Azure Machine Learning and Azure AI Foundry.
• You'll build reusable MLOps pipelines, CI/CD workflows, containerized solutions, APIs, and automation to streamline production deployments.
• You'll establish monitoring and observability practices to track model performance, identify drift, troubleshoot issues, and support retraining.
• You'll apply classification, regression, clustering, feature engineering, and hyperparameter tuning techniques to optimize model performance.
• You'll collaborate with stakeholders to translate business objectives into scalable AI solutions, measurable outcomes, and effective validation strategies.
• You'll support data preparation, quality assurance, experimentation, and resource planning while managing competing priorities and deadlines.

Skills and Qualifications

• Extensive hands-on experience in machine learning engineering, including successfully deploying and supporting models in production environments.
• Advanced expertise in Azure Machine Learning (ML Studio), Microsoft Foundry/Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, and model observability.
• Strong programming and technical skills with Python, SQL, Databricks, Power BI, Power Automate, VS Code, Git, and Jira.
• Deep understanding of machine learning algorithms, statistics, classification, regression, clustering, feature engineering, model evaluation, and hyperparameter optimization.
• Demonstrated experience with model drift, retraining, performance optimization, production troubleshooting, data validation, and preprocessing.
• Strong analytical, communication, stakeholder management, and collaboration skills, with the ability to deliver results across competing priorities.

All qualified applicants will receive fair consideration for employment. We welcome individuals of all backgrounds, experiences, and identities including those who identify as women, members of racialized groups, Indigenous Peoples, persons with disabilities, and 2SLGBTQIA+ communities. If you require an accommodation, please review our accessibility policy and reach out to our accessibility officer with any questions. Our human recruiters review all applications and always make the final hiring decision. On occasion, we also use AI‑assisted tools to help review applications.