Strong proficiency in Python, Git, Bash scripting, Docker and ML flow/Airflow/Kubeflow, Data bricks
MLOps/DevOps Working knowledge with implementation and support experience - 1 or 2 projects a minimum
Hands-on experience in building and maintaining end-to-end ML pipelines including CI/CD pipeline, ML pipeline, alerts and others.
Extensive experience with Azure/AWS/GCP
Well verse in refactoring code and industry coding standards
Agile framework etrained to manage team effort and track through JIRA
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
