Key Responsibilities
- Develop and deploy machine learning models for classification, regression, and clustering tasks
- Design and implement scalable data pipelines for training and validation processes
- Optimize models for performance and efficiency in production environments
- Collaborate with cross-functional teams to integrate ML solutions into existing systems
- Monitor and maintain model performance, ensuring accuracy and reliability
- Leverage cloud infrastructure platforms like Azure or GCP for scalable ML deployments
Requirements
- Strong foundation in Python and machine learning frameworks (Scikit-learn, TensorFlow, Keras)
- Experience with data processing tools (Pandas, NumPy) and SQL for data querying
- Proficiency in cloud platforms (Azure, GCP) and ML tools (Databricks Delta Lake, MLflow)
- Understanding of generative AI and large language models (LLMs)
- Ability to write production-quality code and work with version control systems (Git, GitHub)