Job Title: Machine Learning Engineer
Location: SFO, CA (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
- Develop and deploy machine learning models for customer churn prediction.
- Build and optimize ML models using XGBoost.
- Develop ML pipelines using Python.
- Use AWS SageMaker for model training, deployment, and monitoring.
- Implement model tracking, experiment management, and versioning using MLflow or Weights & Biases.
- Automate model deployment, monitoring, and retraining workflows.
- Collaborate with Data Scientists and Data Engineers to productionize ML solutions.
Required Skills
- Strong hands-on experience in Python.
- Practical experience with XGBoost and churn prediction/modeling.
- AWS SageMaker (training, deployment, endpoints).
- MLflow or Weights & Biases (W&B).
- Experience building end-to-end ML pipelines and deploying models into production.
Skills: XGBoost/churn modeling, Python, AWS Sagemaker, and MLflow/weights & Biases for the MLOps.
