Roles & Responsibilities:
Model and Data Pipelines
Develop automated processes for large scale data pipelines, model development,
operationalization and model monitoring.
Develop and automate the MLOps pipeline.
Deployment on low code env and developing integrations
Take responsibility for production issues, perform root cause analysis, and recommend
changes to reduce/eliminate re-occurrence of issues.
Accurately apply technical job knowledge and skills to complete all work in a timely
manner in accordance with policies, procedures and regulatory requirements
Automate and Improve Efficiency
Automate monitoring of models both for accuracy degradation and failures
Optimize deployment and change control processes for models
Automate logging of model usage and predictions provided
Research, Evolve and publish best practices
Recommend model refinements to optimize cloud spend
Enrich existing ML frameworks and libraries
Research and operationalize technology and processes necessary to scale ML models
Ability to research and recommend best practices on new technologies, platforms and
services
Improve ML pipeline documentation and understandability
Communication and Collaboration
Collaborate with technical teams like data scientist, data developers, development and
platform
Knowledge sharing with the broader analytics team and stakeholders is essential
Communicate on the on-goings to embrace the remote and cross geography culture
Align on the key priorities and focus areas
Ability to communicate the accomplishments, failures and risks in timely manner
Embrace learning mindset
Continually invest in your own knowledge and skillset through formal training, reading,
and attending conferences and meetups
Keyskills: gcp ml engr ml flow machine learning operations ml pipeline gcp vertex ai machine learning engineer operationalize model mlops