Description

You will bridge the gap between data science and production engineering by deploying and managing machine learning pipelines.

Responsibilities

• Productionize machine learning pipelines to move models from experimentation to live environments.
• Build and maintain automated CI/CD workflows for machine learning lifecycles.
• Implement feature engineering processes and model evaluation frameworks.
• Monitor ML model performance and conduct A/B testing in production.

Required Skills

• Over 10 years of professional experience in technical roles.
• Expertise in Python with deep knowledge of Pandas, NumPy, and Scikit-learn.
• Hands-on experience with Databricks, including PySpark and Delta Lake.
• Proficiency in Snowflake using SQL and Snowpark.
• Experience managing the ML lifecycle with MLflow.
• Practical experience with CI/CD for machine learning.
• Working knowledge of cloud platforms including Azure, AWS, or GCP.
• Proficiency with Git and version control.
• Experience with Docker and Kubernetes.

Preferred Skills

• Direct experience in productionizing ML pipelines for 2-3 years.

Key Skills

Education

Any Graduate