Senior Machine Learning Engineer
Core
Design, build, and maintain end-to-end machine learning pipelines from research through production deployment, focusing on scalable training, inference, and retraining workflows.
Role type
Senior Machine Learning Engineer (MLOps)
Builds
Scalable ML pipelines, feature engineering/storage systems, and automated deployment workflows for production environments.
Domain
Financial services / Machine Learning Engineering
Deliverable
production ML models
Required skills
Python, PyTorch, TensorFlow, AWS SageMaker, MLOps (CI/CD, model versioning, monitoring), feature engineering, software engineering best practices
Preferred skills
Graduate degree, experience with batch/real-time/event-driven architectures
Technologies
AWS SageMaker, Pandas, NumPy, Scikit-Learn, PyTorch, TensorFlow
Responsibilities
Design end-to-end ML pipelines; Engineer scalable training and inference workflows; Develop feature engineering and data preparation pipelines; Automate model deployment and release processes; Implement model monitoring for drift and data quality; Partner with researchers to productionalize models; Manage model versioning and experiment tracking; Optimize model performance and cloud cost efficiency.
Seniority
Senior, hands-on IC