Senior MLOps & Cloud Engineer (AWS / SageMaker)

Toronto, Ontario

On-site

Full-time

No salary posted4 days ago

Apply Now

Employment type: Full-time · On-site
Posted: November 9, 2026
Province: Ontario (ON)

Hiring confidence: Sparse posting · 22/100

This posting is missing most of the signals we look for, or shows patterns common to listings that are not actively being filled. Check with the employer before applying. How this is scored

Job Overview

Jobs

This is only part of this posting — the job board it came from publishes a short summary. Read the full posting.

Our client is a premier financial services organization that has been helping Canadian investors build better financial futures for over 35 years. As a privately owned company, they boldly embrace innovation across their business while fostering a diverse, supportive, and inclusive culture. Role Overview This Senior MLOps & Cloud Engineer position exists to bridge machine learning model deployment, data transformation pipelines, and AWS cloud infrastructure. In this hands-on role, you will take…

This role is based in Ontario. Randstad Digital is hiring for this full-time position through Jobily, where you can check how well your resume matches the posting before you apply.

Market context

About this role in Ontario

Average salary for Senior MLOps & Cloud Engineer (AWS / SageMaker) in Ontario

We do not have a salary benchmark for this title in Ontario yet. The posting does not state a salary either.

NOC code

This posting has not been matched to a NOC 2021 unit group yet. Use the NOC finder to identify the code from the duties listed above.

Typical qualifications for Canadian employers

Senior-level experience with AWS cloud platform
Expertise in SageMaker and ML model deployment
Strong background in MLOps practices
Experience with data transformation pipelines
Proficiency in infrastructure automation

Fit check

Is this role right for you?

Similar jobs

What you'll do

Bridge machine learning model deployment with cloud infrastructure
Design and maintain data transformation pipelines
Manage AWS cloud infrastructure for ML workloads
Deploy and monitor machine learning models using SageMaker
Implement MLOps best practices and frameworks
Collaborate on infrastructure scaling and optimization

What you'll need

Senior-level experience with AWS cloud platform
Expertise in SageMaker and ML model deployment
Strong background in MLOps practices
Experience with data transformation pipelines
Proficiency in infrastructure automation
Knowledge of containerization and orchestration