Engineer - MLOps & Scientific Platforms - Data Foundry
US: South San Francisco, United States
USD 66K-165K Mid-level Full Time
Tasks
- Build ML deployment pipelines
- Build cloud native model serving using containers and Kubernetes
- Build dashboards and metrics tracking
- Build model serving infrastructure for synchronous and asynchronous workloads
- Collaborate on API performance requirements
- Define API contracts and documentation and testing frameworks
- Deploy predictive and analytical methods with versioning and error handling
- Develop CI/CD pipelines for ML models
- Expose scientific tools through REST APIs
- Implement model registry and feature engineering pipelines
- Implement structured logging and tracing
- Integrate model serving with data pipelines
- Maintain model versioning and experiment tracking
- Provide uncertainty quantification and confidence metrics
- Set up monitoring and alerting for data pipelines and ML models
Perks/Benefits
- N/A
Skills/Tech-stack
API Gateway | AWS | Automated retraining | Azure | CI/CD | CUDA | DVC | Data Versioning | Docker | Drift Detection | Event Driven | Event-driven architecture | Feature Engineering | Feature Store | GCP | GPU Computing | GRPC | Kubeflow | Kubernetes | Langchain | ML model monitoring | MLflow | Model Monitoring | PyTorch | Python | REST API | Scikit-learn | Service Mesh | TensorFlow | Weights and Biases
Education
Regions
Countries
States
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