Senior Machine Learning Engineer
Tasks
- Build reusable MLOps frameworks
- Collaborate with data scientists on production ready models
- Design and implement end-to-end machine learning pipelines
- Document machine learning standards patterns and best practices
- Ensure production performance reliability and availability
- Ensure secure auditable pipelines
- Establish model performance monitoring and alerting
- Evolve MLOps toolchain
- Implement machine learning continuous integration and deployment
- Integrate machine learning outputs into downstream systems
- Manage compute resources and environment configurations
- Mature AI and ML engineering discipline
- Operationalize machine learning models
- Provide guidance on feature engineering and model packaging
- Support real-time and batch inference
Perks/Benefits
- Dental insurance
- Disability insurance
- EAP
- Life insurance
- Medical insurance
- Paid time off
- Remote work flexibility
- Technology stipend
- Tuition reimbursement
- Vision insurance
Skills/Tech-stack
AWS | Artifact management | Auditability | Azure | Batch inference | Compute resource management | Continuous Deployment | Continuous integration | Cost controls | Data Ingestion | Data Pipelines | Data Quality | Data quality monitoring | Databricks | Drift Detection | Experiment tracking | Feature Engineering | MLOps | Machine Learning | Machine learning services | Managed Machine Learning Services | Model Deployment | Model Drift | Model Drift Detection | Model Monitoring | Model Training | Model Validation | Model versioning | PyTorch | Python | Quality monitoring | Real Time | Real-time Inference | Resource Management | Secure Data | Secure data pipelines | TensorFlow | Workflow Orchestration
Education
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