MLOps Engineer
Automate ML training, deployment, and monitoring pipelines on Databricks, SageMaker, and cloud platforms.
Generative AICanada / USA (Remote / Hybrid)Remote / HybridNow Hiring
About the role
Help clients operationalize machine learning with repeatable CI/CD workflows, model registries, and production monitoring. You will work on computer vision, NLP, and predictive analytics programs at enterprise scale.
Key responsibilities
- Design end-to-end MLOps workflows from dataset versioning through model deployment and monitoring.
- Implement CI/CD pipelines for ML using GitLab, GitHub Actions, Terraform, or cloud-native tooling.
- Build and optimize inference pipelines with Docker, SageMaker, Databricks, or Triton/Ray where applicable.
- Establish metadata tracking, lineage, and reproducibility standards for model delivery.
- Support data science teams with scalable feature engineering and retraining automation.
Qualifications
- 4+ years of experience in data engineering, ML engineering, or platform engineering roles.
- Hands-on experience with at least one ML platform (SageMaker, Databricks ML, Azure ML).
- Proficiency in Python and experience containerizing ML workloads.
- Understanding of model monitoring, drift detection, and production support practices.
Nice to have
- Experience with MLflow, Feature Store, or hyperparameter tuning at scale.
- Familiarity with TensorRT, batch/real-time inference optimization.