Lead ML Engineer
Tasks
- Build monitoring dashboards for model performance
- Define model lifecycle standards
- Design reusable feature frameworks and curated datasets
- Design scalable ML model deployment
- Embed pricing conversion and risk models into underwriting workflows
- Ensure audit readiness and model documentation
- Implement batch and API scoring
- Implement model monitoring drift detection and alerting
- Improve data reliability and engineering maturity
- Integrate AI use cases into insurance workflows
- Lead code reviews pair programming and architecture oversight
- Operationalize GenAI deployments
- Set CI CD standards for analytics pipelines
- Standardize development practices and tooling
Perks/Benefits
- Employee assistance program
- Employee stock purchase plan
- Hybrid work
- Learning and development
- Life insurance
- Paid time off
- Pension plan
- Private medical insurance
- Subsidised gym membership
Skills/Tech-stack
API Development | AWS SageMaker | Automation | Azure | Azure Machine Learning | Batch Scoring | CI/CD | Cloud Computing | Data Drift | Data Drift Detection | Data Governance | Drift Detection | Feature Engineering | Feature Store | MLflow | Machine Learning | Model Monitoring | Model versioning | Python | Retraining | Testing
Education
N/A
Roles
Data Scientist | Engineer | Learning Engineer | ML Engineer | Machine Learning Engineer | Scientist
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