Overview
Remote
$60 - $65
Contract - W2
Contract - 4 Month(s)
Skills
MLOps
Machine Learning Operations
AI/ML Model Deployment
ML Model Lifecycle Management
Feature Engineering Pipelines
Microsoft Azure
Azure Cloud
Azure Machine Learning
Azure Kubernetes Service
Databricks
GitHub Actions
CI/CD Pipelines
Infrastructure as Code
Kubernetes
Docker
Terraform
Job Details
Job Title: MLOps Engineer Location: 100% remote ( USA ) - EST Duration: 4 month contract ( potential for extension ) Visa : / L2/ / GC Need 12+ Years of experience
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
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.