Senior MLOps Engineer
New
J
JobgetherAI infrastructure
Based in CanadaFull-TimeSenior
Salary150,000 - 200,000 CAD per year
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Job Details
- Experience
- 6–10+ years of experience in software engineering or machine learning engineering, including at least 3 years focused on MLOps or machine learning infrastructure.
- Required Skills
- AWSDockerPythonBashGCPKubernetesMicrosoft AzureMLFlowC++CI/CD
Requirements
- 6–10+ years of experience in software engineering or machine learning engineering, including at least 3 years focused on MLOps or machine learning infrastructure.
- Strong programming skills in Python and experience with C, C++, and Bash.
- Experience deploying, operating, and maintaining machine learning models in production.
- Proficiency with Docker and Kubernetes.
- Hands-on experience with AWS, Google Cloud Platform, or Microsoft Azure, including GPU orchestration.
- Experience designing and maintaining CI/CD pipelines using GitHub Actions, Jenkins, or equivalent tools.
- Familiarity with MLflow, Weights & Biases, Kubeflow, or similar experiment tracking and pipeline automation tools.
- Understanding of data versioning, model reproducibility, production monitoring, and model lifecycle management.
- Willingness to undergo comprehensive background checks and attend occasional in-person company or departmental meetings, typically once or twice per year.
- Model training from scratch, model optimization techniques, infrastructure-as-code, distributed systems, high-performance computing, and open-source contributions are assets or advantages.
Responsibilities
- Design and maintain end-to-end machine learning pipelines for training, evaluation, deployment, and agentic AI workflows.
- Build and optimize cloud infrastructure for model training and serving, including GPU-based workloads.
- Develop tools for data pipelines, data labeling, experiment tracking, model performance measurement, and automated retraining.
- Collaborate with AI researchers and machine learning engineers to move proof-of-concept solutions into production.
- Implement CI/CD practices, automated testing, and deployment workflows for machine learning systems.
- Monitor production models for performance, health, data quality, and model drift.
- Improve infrastructure provisioning, data access, and reliability of shared technical systems.
- Optimize infrastructure costs and performance for large-scale training and inference.
- Establish documentation, technical standards, and best practices for MLOps workflows and infrastructure.
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