Design the data pipelines and engineering infrastructure to support our clients' enterprise machine learning systems at scale
Take off the model's that data scientists build and turn them into a real machine learning production system.
Develop and deploy scalable tools and services for our clients to handle machine learning training and inference.
Identify and evaluate new technologies to improve performance, maintainability, and reliability of our clients' machine learning systems.
Apply software engineering rigor and best practices to machine learning, including CI/CD, automation, etc.
Support model development, with an emphasis on auditability, versioning, and data security.
Maintain and develop a data feature store that data scientists can use to prepare the data set required for the modeling by combining data from different domains and sources across the organization.
Monitoring the model performance in production and ability to develop model drift methodologies
DanaRemoters