MLOps Engineer – Hourly and Full time
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Job Description
Senior ML Engineer guiding ML pipelines and model serving infrastructure for an international client. Strong data engineering foundation and hands-on MLOps experience required.
Responsibilities:
- Design and maintain end-to-end ML pipelines from data ingestion to model deployment
- Operate model registries, feature stores, and experiment tracking (MLflow, W&B)
- Build scalable model serving infrastructure on Kubernetes and cloud platforms
- Implement CI/CD workflows for ML models, including testing and rollback strategies
- Monitor production models — drift detection, alerting, and retraining pipelines
- Collaborate with data scientists and platform engineers to ship ML solutions faster
Requirements:
- 5+ years in data engineering (pipelines, warehouses, orchestration)
- 2+ years of hands-on ML engineering / MLOps in production environments
- Strong Python skills and experience with Airflow, Spark, or similar orchestration tools
- Solid knowledge of Kubernetes, Docker, and at least one major cloud (AWS, GCP or Azure)
- Familiarity with ML tooling: MLflow, W&B, DVC, or equivalent
- Available full-time (80h/week), EST-aligned, eligible to contract in Canada (CAD)
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