About this role
- Build reusable Python modules for:
- Data preparation
- Feature engineering
- Model training
- Hyperparameter tuning
- Model evaluation
- Model scoring
- Model validation
- Develop configurable workflows to reduce model development turnaround time.
Responsibilities
- Build end-to-end automated pipelines.
- Develop scalable ETL/ELT workflows using Python and PySpark.
- Automate data ingestion, transformation, validation, and feature generation processes.
- Implement metadata-driven and reusable pipeline architectures.
MLOps & Deployment
- Develop and maintain CI/CD pipelines for ML solutions.
- Package and deploy models as APIs, batch services, or containerized applications.
- Integrate models with underwriting, claims, and pricing systems.
Implement model versioning, model registry integration, and release management processes
Qualifications
Bachelor's/Master's in Engineering 3-6 years
Tired of cold applications?
Sign up with Clera and we'll reach out the moment a role actually fits you — no more spraying applications into the void.
Know someone who'd be great for this?