It's a backfill position — MLOps Engineer — Remote !!
MLOps Engineer
Remote
Remote
Max Pay rate: $60/hr on C2C All INC
Skillset: Kubernetes, AKS, Azure cloud, Azure DevOps, Databricks
Nice to have: MLOps, ML Infrastructure deployments
Description:
We are looking for an experienced Machine Learning Operations Engineer who has experience working with design, development and implementation of AI/ML applications and managing the lifecycle of Machine Learning models.
The role of an MLOps Engineer is at intersection of Data Scientist, Data Engineer, and DevOps Engineer. You'll 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 and deploy, making it available to other applications.
We are looking for an experienced Machine Learning Operations Engineer who has experience working with design, development and implementation of AI/ML applications and managing the lifecycle of Machine Learning models.
The role of an MLOps Engineer is at intersection of Data Scientist, Data Engineer, and DevOps Engineer. You'll 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 and deploy, making it available to other applications.
Job Responsibilities:
Design and deploy scalable infrastructure for ML workloads using cloud platforms and containerization technologies (e.g., Docker, Kubernetes)
Work with teams to design and build cloud hosted, automated pipelines that run, monitor, and retrain ML Models for business applications
Design and implement Model and Pipeline validation procedures alongside teams of Data Scientists, Data Engineers, and other ML Engineers
Optimize and refactor development code so that it can be moved to production
Build Data, Feature Engineering Pipelines for new and existing models
Assemble configurations and specifications to automatically build environments in production
Create and develop in CI/CD Pipelines which allow for controlled and continuous enhancement of existing work and new features during both development and production phases
Design and deploy scalable infrastructure for ML workloads using cloud platforms and containerization technologies (e.g., Docker, Kubernetes)
Work with teams to design and build cloud hosted, automated pipelines that run, monitor, and retrain ML Models for business applications
Design and implement Model and Pipeline validation procedures alongside teams of Data Scientists, Data Engineers, and other ML Engineers
Optimize and refactor development code so that it can be moved to production
Build Data, Feature Engineering Pipelines for new and existing models
Assemble configurations and specifications to automatically build environments in production
Create and develop in CI/CD Pipelines which allow for controlled and continuous enhancement of existing work and new features during both development and production phases
Must-Have Skills:
Kubernetes, AKS
Azure cloud, Azure DevOps, Databricks
MLOps, ML Infrastructure deployments
Education Level Required: [Insert Minimum Education Level] Will Candidate be asked Coding Questions in Initial Round: [Yes]
Kubernetes, AKS
Azure cloud, Azure DevOps, Databricks
MLOps, ML Infrastructure deployments
Education Level Required: [Insert Minimum Education Level] Will Candidate be asked Coding Questions in Initial Round: [Yes]
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