MLOps Engineer
New
Australia, Canada, Ireland, New Zealand, United Kingdom, United States, EST-alignedContractSenior
Salary not disclosed
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Job Details
- Experience
- 5+ years in data engineering; 2+ years of hands-on ML engineering
- Required Skills
- AWSDockerPythonGCPKubernetesMLFlowAirflowAzureSpark
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
- At least one major cloud (AWS
- GCP or Azure)
- Familiarity with ML tooling: MLflow
- W&B
- DVC
- Or equivalent.
Responsibilities
- Design and maintain end-to-end ML pipelines from data ingestion to model deployment
- Operate model registries
- Feature stores
- Experiment tracking
- 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 for drift detection and retraining pipelines
- Collaborate with data scientists and platform engineers to ship ML solutions.
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