Data Scientist

Responsibilities:

• Develop and maintain model inference pipelines (batch and real-time) using Python and FastAPI.
• Implement feature engineering pipelines (transformation, encoding, selection) for training and inference consistency.
• Manage model orchestration workflows with tools such as Airflow or equivalent.
• Monitor data drift and model drift, implementing alerts and retraining triggers where needed.
• Collaborate with MLOps engineers to ensure smooth deployment and scaling of ML models.
• Optimize inference speed, latency, and resource usage across cloud and containerized environments.
• Work with CI/CD pipelines to automate the integration of new models into production systems.

Requirements:

• Should have worked on the infrastructure side of multiple machine learning projects, ideally involving deep learning models such as BERT and Transformers.
• Experience in managing resources and optimizing performance for large-scale machine learning tasks.
• Experience in managing and optimizing GPU resources, both on-premise and in the cloud, for machine learning tasks.
• Experience in handling the challenges associated with training large models, such as managing memory usage, optimizing data loading, and troubleshooting hardware issues.
• Ideally, it should have worked closely with data scientists and machine learning engineers to understand their infrastructure needs and provide efficient solutions.