Machine Learning Engineer, Underwriting
Core
Design, develop, and deploy end-to-end machine learning pipelines to power critical underwriting decisions for a consumer finance platform.
Role type
Machine Learning Engineer (MLOps & Model Deployment)
Builds
Production ML models and data pipelines for credit underwriting
Domain
Fintech / Consumer Finance
Deliverable
production ML models
Required skills
Python, Scikit-learn, LightGBM, PyTorch, MLOps (MLflow, Kubeflow, SageMaker), SQL, NoSQL, Docker, Kubernetes, AWS/GCP/Azure, feature engineering, hyperparameter tuning
Preferred skills
Real-time and batch inference pipeline implementation, bias detection, model explainability
Technologies
Pandas, NumPy, MLflow, Kubeflow, SageMaker, Docker, Kubernetes, AWS, GCP, Azure
Responsibilities
Design and deploy end-to-end ML pipelines; Implement MLOps best practices including CI/CD and model versioning; Optimize ML models using feature engineering and hyperparameter tuning; Collaborate with data engineers on high-performance data pipelines; Deploy and manage models on cloud platforms with containerization; Maintain model performance via continuous monitoring and explainability techniques
Seniority
Mid-level, hands-on IC
