As of November 6, 2026, Jobsy lists 6 mlops engineer jobs in Canada. The most active employers are Fluent, LLC, CGI and Clariti Cloud Inc.. Skills often requested: ci/cd, aws, python and docker.
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Fluent, LLC: (NASDAQ: FLNT) is a commerce media solutions provider connecting top-tier brands with highly engaged consumers.
Candidates need strong Databricks and AWS machine learning experience, proficiency in Python, SQL and PySpark, and hands-on work with MLflow, CI/CD and monitoring. They also need proven ownership of real-time, low-latency model serving and experience reviewing code and mentoring engineers.
Databricks, AWS, MLflow, Delta Live Tables, PySpark, Python
Fluent, LLC: (NASDAQ: FLNT) is a commerce media solutions provider connecting top-tier brands with highly engaged consumers.
Candidates need strong experience with Databricks and AWS, Python, SQL and PySpark, plus MLflow and CI/CD. They must have owned real-time, low-latency model serving and be able to resolve production problems independently.
Databricks, AWS, MLflow, Delta Live Tables, PySpark, Python
6+ YOECandidates need 6 to 10+ years in software or ML engineering, including 3+ in MLOps, with production model deployment experience. Strong Python, Docker, Kubernetes and cloud skills are required.
Insight Global: Insight Global is an international talent and consulting company that delivers business outcomes in an ever-changing world.
5+ YOECandidates need 5+ years in DevOps, MLOps, SRE, platform or cloud engineering, with hands-on production Kubernetes experience. They also need 2-4 years with Docker, Helm, ArgoCD and CI/CD, plus Kubeflow or similar and Azure AI experience.
Candidates need hands-on AWS experience (SageMaker is a strong asset), proven deployment of machine learning models to production, and deep senior-level Python skills. A solid grasp of RAG, agents and classical machine learning is also required, and Capital Markets experience is a strong plus.
4+ YOEPlanitar needs candidates with 4+ years of MLOps or ML software experience, including models taken to production. They should have strong Python, PyTorch and ONNX skills, container and CI/CD experience, and be comfortable owning a platform alone.