Machine Learning Engineer

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.