Job Title: Machine Learning/MLOps Engineer
Location: SFO, CA, Columbus, OH, Jersey City, NJ (Hybrid – 3 days from office)
Duration: 12 Months
Role Overview
We are looking for a hands-on ML Ops Engineer with strong experience in machine learning model development, deployment, and productionization. The ideal candidate should have practical experience with churn prediction/modeling, XGBoost, Python, AWS SageMaker, and MLOps tools such as MLflow or Weights & Biases.
Key Responsibilities & Skills:
· Strong Python programming with hands-on experience in XGBoost, predictive analytics, and churn modeling.
· Expertise in AWS SageMaker for model training, deployment, optimization, and monitoring.
· Experience with MLflow and Weights & Biases (W&B) for experiment tracking, model registry, and versioning.
· Experience with HPE Swarm Learning for decentralized, privacy-preserving, distributed model training.
· Knowledge of model explainability and feature importance.
· Build automated ML pipelines, CI/CD workflows, model monitoring, drift detection, and retraining processes.
· Experience with Docker, AWS S3, Git, and integrating ML models into enterprise applications.
· Collaborate with data scientists and engineering teams to support the Algorithm Workbench, reproducibility, data integrity, and GxP compliance.
Skills: XGBoost/churn modeling, Python, AWS Sagemaker, and MLflow/weights & Biases for the MLOps.
