Overview
Remote
$65 - $68
Full Time
Skills
Kubernetes
AKS
Azure cloud
Azure DevOps
Databricks
MLOps
ML Infrastructure deployments
Job Details
Must be able to work on W2 and should have MINIMUM of 9+ years of experience overall.
Must have Skillset : Kubernetes, AKS, Azure cloud, Azure DevOps, Databricks
Nice to have : MLOps, ML Infrastructure deployments
We are seeking for an experienced ( ML ) Machine Learning Operations Engineer and experience working with design, development & implementation of AI / ML applications & managing the lifecycle of Machine Learning models.
The role is MLOps Engineer, intersection of Data Scientist, Data Engineer, & DevOps Engineer. You will be working in a team of engineers that takes on a wide array of responsibilities that encompass building all the infrastructure necessary to take a trained ML Model , integrate & deploy, making it available to other applications.
RESPONSIBILITIES
Design & deploy scalable infrastructure for ML workloads using cloud platforms & containerization technologies ( e.g., Docker, Kubernetes )
Need to Work with teams, to design & build cloud hosted, automated pipelines that run, monitor, & retrain ML Models for business applications
Design & implement Model & Pipeline validation procedures alongside teams of Data Scientists, Data Engineers, & other ML Engineers
Optimize & refactor development code so that it can be moved to production
Need to Build Data, Feature Engineering Pipelines for new & existing models
Assemble configurations & specifications to automatically build environments in production
Create & develop in CI / CD Pipelines which allow for controlled & continuous enhancement of existing work & new features during both development & production phases
Must have Skillset : Kubernetes, AKS, Azure cloud, Azure DevOps, Databricks
Nice to have : MLOps, ML Infrastructure deployments
We are seeking for an experienced ( ML ) Machine Learning Operations Engineer and experience working with design, development & implementation of AI / ML applications & managing the lifecycle of Machine Learning models.
The role is MLOps Engineer, intersection of Data Scientist, Data Engineer, & DevOps Engineer. You will be working in a team of engineers that takes on a wide array of responsibilities that encompass building all the infrastructure necessary to take a trained ML Model , integrate & deploy, making it available to other applications.
RESPONSIBILITIES
Design & deploy scalable infrastructure for ML workloads using cloud platforms & containerization technologies ( e.g., Docker, Kubernetes )
Need to Work with teams, to design & build cloud hosted, automated pipelines that run, monitor, & retrain ML Models for business applications
Design & implement Model & Pipeline validation procedures alongside teams of Data Scientists, Data Engineers, & other ML Engineers
Optimize & refactor development code so that it can be moved to production
Need to Build Data, Feature Engineering Pipelines for new & existing models
Assemble configurations & specifications to automatically build environments in production
Create & develop in CI / CD Pipelines which allow for controlled & continuous enhancement of existing work & new features during both development & production phases
Employers have access to artificial intelligence language tools (“AI”) that help generate and enhance job descriptions and AI may have been used to create this description. The position description has been reviewed for accuracy and Dice believes it to correctly reflect the job opportunity.