Remote MLOps Engineer Jobs in Canada
Search 3 work from home mlops engineer positions available in Canada. Your career in truly remote companies - apply today.
November 19
Founding MLOps developer at Wealthsimple enhancing AI/ML pipelines for financial services. Lead the development of secure and scalable machine learning infrastructure.
August 8
Job not on LinkedIn
Join Stratum as a Machine Learning Ops Engineer to enhance our mining AI platform. Collaborate with a team to develop robust ML infrastructure and tools.
March 20
Scopic
201 - 500
Join Scopic as a Remote MLOps Consultant to optimize MLOps workflows and build scalable infrastructures.
Remote mlops engineer salary expectations in Canada
The average salary for remote mlops engineers in Canada is CA$129,583 per year. This is based on data from 6 job openings. Our advanced AI searches the internet for remote job openings and posts them on our website. We use the salary data from these job postings to calculate salary expectations.
Below is a breakdown of salary data by years of experience:
| Experience | Number of roles analyzed | Average Salary |
|---|---|---|
🟢 Junior MLOps Engineer (1-2 yrs) | 1 | CA$0 |
🟡 Mid-level MLOps Engineer (2-4 yrs) | 4 | CA$194,375 |
🔴 Lead MLOps Engineer (10+ yrs) | 1 | CA$0 |
Frequently asked questions
You need strong programming skills in Python or R, expertise in machine learning frameworks like TensorFlow or PyTorch, proficiency in cloud services (AWS, Azure, GCP), and knowledge of CI/CD practices. Familiarity with data pipelines, containerization (Docker, Kubernetes), and strong problem-solving skills are also important.
Typically, a degree in computer science, data science, or a related field is required. Experience with machine learning projects and a strong portfolio of work are often more important than formal education. Certifications in cloud platforms and MLOps methodologies can enhance your qualifications.
Responsibilities include deploying machine learning models, automating workflows, monitoring model performance, collaborating with data scientists and developers, and ensuring scalability and reliability of ML applications. You'll also need to maintain data integrity and adherence to best practices.
Benefits include flexibility in work hours, the ability to work from diverse locations, reduced commuting costs and time, and an enhanced work-life balance. Remote roles also provide opportunities to work with cutting-edge technologies and collaborate with a global talent pool.
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Frequently asked questions
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