Machine Learning Operations-Engineer I
About The Position
Develop enterprise-wide and scalable cloud-based MLOps capabilities that span the full life cycle of analytical models. Develop reusable, secure, and robust ML pipelines, monitor model performance, monitor data drift, utilize insights to train models, enable automatic audit trails creation for all artifacts, deploy across a wide range of business applications, and sustain a high level of automation across all ML life cycle activities. This includes developing code and making sure that ML models are production ready. Continuously improve the speed, quality, and efficiency of model/experiments development, production, and maintenance. Collaborate with Model Management/Governance to develop and maintain enterprise wide MLOps standards. Collaborate with internal stakeholders and vendors in developing MLOps solutions that meet business requirements across a variety of areas including, but not limited to, Data Science, IT, cybersecurity, compliance, and Legal. Maintain up to date knowledge about the latest advances in MLOps, engage stakeholders, and champion proactive measures to sustain a cost effective, efficient, and innovative MLOps capabilities. Develop and maintain a deep understanding of business requirements to ensure that MLOps solutions deliver practical and timely value. Conduct MLOps research and proof of concept projects to improve practice and develop business cases that support business needs Develops and apply algorithms that generate success metrics to improve the value of MLOps. Presents findings and analysis for use in decision making and demonstrate bottom-line financial benefits. Collaborate with Cloud Solution Architects in developing solutions. Prioritizes tasks and meets project deadlines in a fast paced work environment.
Responsibilities
- Develop enterprise-wide and scalable cloud-based MLOps capabilities that span the full life cycle of analytical models.
- Develop reusable, secure, and robust ML pipelines, monitor model performance, monitor data drift, utilize insights to train models, enable automatic audit trails creation for all artifacts, deploy across a wide range of business applications, and sustain a high level of automation across all ML life cycle activities.
- Continuously improve the speed, quality, and efficiency of model/experiments development, production, and maintenance.
- Collaborate with Model Management/Governance to develop and maintain enterprise wide MLOps standards.
- Collaborate with internal stakeholders and vendors in developing MLOps solutions that meet business requirements across a variety of areas including, but not limited to, Data Science, IT, cybersecurity, compliance, and Legal.
- Maintain up to date knowledge about the latest advances in MLOps, engage stakeholders, and champion proactive measures to sustain a cost effective, efficient, and innovative MLOps capabilities.
- Develop and maintain a deep understanding of business requirements to ensure that MLOps solutions deliver practical and timely value.
- Conduct MLOps research and proof of concept projects to improve practice and develop business cases that support business needs
- Develops and apply algorithms that generate success metrics to improve the value of MLOps.
- Presents findings and analysis for use in decision making and demonstrate bottom-line financial benefits.
- Collaborate with Cloud Solution Architects in developing solutions.
- Prioritizes tasks and meets project deadlines in a fast paced work environment.
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
No Education Listed
Number of Employees
5,001-10,000 employees
