Senior ML Engineer (MLOps)
Tasks
- Audit and migrate production models
- Create standard library wrapper layer
- Define MLOps lifecycle
- Deploy feature store proof of concept
- Eliminate train serve skew
- Enforce CI CD gates
- Implement model monitoring
- Integrate lineage and eval harness
- Lead feature store evaluation
- Lead incident response
- Mentor data scientists
- Operate model registry
- Partner with Data Governance
- Retire or consolidate models
- Set alerting and retraining triggers
- Support audit compliance evidence
- Version and tag production models
Perks/Benefits
- N/A
Skills/Tech-stack
A/B | A/B Testing | Amazon SageMaker | B testing | CI/CD | Causal Experimentation | Causal Inference | Data Versioning | Databricks Feature Store | Databricks Unity | Databricks Unity Catalog | DoWhy | Drift Detection | EconML | Experiment tracking | Feast | Feature Store | Geographic Experiments | Incident Response | Kubeflow | LightGBM | MLflow | Model Drift | Model Drift Detection | Model Monitoring | Prophet | PySpark | Python | SQL | Scikit-learn | Spark | StatsModels | Switchback testing | Tecton | Unity Catalog | Vertex AI | XGBoost
Education
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