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)
- 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 recent topics, services and enhancements in Cloud Technologies