CLDRSRC10410 - Machine Learning Engineer - Toronto - Hybrid

Indotronix UK; Toronto; Híbrido; CAD 90 000 - 150 000; Tempo integral

Resumo da oferta

Cloud Resourcing is seeking a Machine Learning Engineer to join our Toronto team on a hybrid basis. The role emphasizes production-grade ML engineering, deployment, monitoring, and collaboration with cross-functional teams.

The ideal candidate will have deep hands-on experience with Azure Machine Learning, MLOps, and related tools, and will contribute to scalable ML solutions in a fast-paced environment.

Qualificações

• Strong Python and ML engineering skills with production deployment experience.
• Experience with Azure ML, Foundry/AI Foundry, MLOps, and CI/CD.
• Ability to deploy, monitor, and observe models in production environments.

Responsabilidades

• Take models from experimentation to reliable production environments.
• Build and maintain ML pipelines and APIs for production use.
• Collaborate with data scientists and engineers to ensure scalable ML solutions.

Conhecimentos

Python programming

ML engineering

CI/CD

APIs

Ferramentas

Azure Machine Learning

Databricks

Power BI

Descrição da oferta de emprego

CLDRSRC10410 - CLDRSRC10410 - Machine Learning Engineer - Toronto - Hybrid

[Toronto, ON, M5A 3N7], Canada

10 - 15 Years

N/A

Posted 2 days ago

About Company

Cloud Resourcing is IT focused recruitement firm with focus on finding the bext role for the qualified candidates. We are seeking qualified resource for the following:

Job Description

Mandatory Experience:

• Advanced hands-on experience with Azure Machine Learning, Microsoft Foundry/Azure AI Foundry, MLOps, CI/CD, containers, APIs, automation, model deployment, monitoring and observability.
• Strong Python and machine-learning engineering skills with demonstrated experience taking models from experimentation into reliable production environments. Tech stack: Python, SQL, Power BI, Power Automate, VSCode, Git, Jira, Azure (ML Studio, AI Foundry), Databricks.
• Deep understanding of ML algorithms and statistics, including classification, regression, clustering, feature engineering, model evaluation, hyperparameter tu...