Machine Learning Operations Engineer

ML Ops

• 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