Senior MLOps Engineer
Tasks
- Build automated model retraining pipelines
- Build automated model training pipelines
- Build machine learning infrastructure
- Collaborate with machine learning scientists
- Deploy models to production
- Design low latency model orchestration pipelines
- Detect data drift
- Detect model drift
- Develop model release processes
- Develop production monitoring systems
- Identify model degradation
- Implement CI/CD for ML workflows
- Implement automated deployment processes
- Implement model calibration frameworks
- Implement model versioning
- Implement reinforcement learning orchestration
- Integrate Databricks workflows
- Maintain machine learning infrastructure
- Manage full model lifecycle
- Operate machine learning models
- Productionize machine learning models
- Scale machine learning infrastructure
- Set up Git based development for ML pipelines
- Support drift mitigation strategies
- Support retraining workflows
- Track model performance metrics
- Validate models before deployment
Perks/Benefits
- Exposure to advanced ML technologies
- Ownership across full ML lifecycle
- Professional development
- Supportive collaboration environment
Skills/Tech-stack
Airflow | Apache Spark | CI/CD | Contextual Bandits | Data Drift | Data Drift Detection | Databricks | Drift Detection | Feature Store | Git | Kubeflow | Low Latency | Low-Latency Systems | MLflow | Model Drift | Model Drift Detection | Model Monitoring | Model calibration | Model versioning | Python | Q-learning | Reinforcement Learning | Retraining Pipelines | SQL | Unity Catalog
Education
N/A
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