Staff Data Engineer
Tasks
- Apply responsible AI practices
- Author Infrastructure-as-Code
- Automate alerting and retraining triggers
- Build CI/CD pipelines for models
- Build model training infrastructure
- Build monitoring for concept drift
- Build monitoring for data drift
- Create documentation standards
- Create feature stores
- Deploy model serving endpoints
- Design MLOps platform
- Detect bias
- Develop self service templates
- Enable experiment lineage
- Enforce ML governance
- Implement approval workflows
- Implement audit trails
- Implement autoscaling
- Implement blue green deployment
- Implement canary deployment
- Implement experiment tracking
- Implement explainability tooling
- Implement model registry
- Manage model artifacts
- Mentor junior engineers
- Optimize cloud costs
- Provision Azure ML resources
- Provision Databricks clusters
- Schedule GPU workloads
- Use spot instances
Perks/Benefits
- N/A
Skills/Tech-stack
Artifact management | Autoscaling | Azure AKS | Azure Cloud | Azure Data | Azure Data Factory | Azure DevOps | Azure Machine Learning | Azure Monitor | Azure Workflows | Bias detection | Bicep | Blue-Green Deployment | Blue/green | CI/CD | Canary Deployment | Concept drift | Concept drift detection | Data Drift | Data Drift Detection | Data Factory | Databricks | Databricks Model Serving | Docker | Drift Detection | Evidently AI | Explainability | GPU scheduling | Grafana | Kubernetes | MLflow | Machine Learning | Model Registry | Model Serving | Model versioning | Prediction Quality | Prediction Quality Monitoring | PyTorch | Python | Quality monitoring | Responsible AI | SQL | Scikit-learn | Spot instances | Terraform | Unity Catalog | XGBoost
Education
Bachelor of Engineering | Bachelor of Science | Master of Science
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