Responsible for successful delivery of MLOps solutions and services in client consulting environments
Define key business problems to be solved formulate high level solution approaches and identify data to solve those problems, develop, analyze/draw conclusions and present to client.
Assist clients with operationalization metrics to track performance of ML Models
Help team with ML Pipelines from creation to execution
Guide team to debug issues with pipeline failures
Understand and take requirements on Operationalization of ML Models from Data Scientist
Engage with Business / Stakeholders with status update on progress of development and issue fix
Setup Standards related to Coding, Pipelines and Documentation
Research on new topics, services and enhancements in Cloud Technologies
Technical and Skilled Requirements:
Machine Learning & MLOps ML lifecycle management Model deployment and monitoring Feature stores and model registries Experiment tracking Tools & Frameworks MLflow Kubeflow Airflow TensorFlow / PyTorch / Scikit-learn Apache Spark (preferred)