Elios Talent
Overview
Build and scale ML infrastructure and deployment pipelines. Empower data scientists with production-ready tools. Optimize model monitoring, retraining, and versioning. Job Information
Location:
Flexible / Remote Employment Type:
Full-Time Compensation:
$130,000 – $210,000 Role Summary
We are seeking an MLOps Engineer to design, implement, and maintain machine learning infrastructure at scale. You will build robust deployment pipelines, automate retraining workflows, and create tools that enable data scientists to efficiently deploy and manage models in production. This role combines engineering expertise with a deep understanding of ML lifecycle management. Key Responsibilities
Build and maintain CI/CD pipelines for machine learning workflows. Implement systems for model monitoring, logging, and performance tracking. Develop and manage feature stores for reliable ML data pipelines. Automate model retraining, versioning, and deployment. Collaborate with data scientists to streamline model deployment at scale. Ensure ML infrastructure reliability using Kubernetes and Terraform. Requirements
4–8 years of experience in MLOps, ML engineering, or related roles. Strong knowledge of CI/CD best practices for machine learning. Hands-on experience with Airflow, MLflow, Feast, and Kubernetes. Familiarity with infrastructure as code (Terraform). Proficiency in Python and ML deployment workflows. Proven ability to support large-scale ML systems in production. About the Opportunity
This role is ideal for someone passionate about building the backbone of machine learning systems. You’ll enable scalable, reliable, and automated ML deployments while collaborating closely with both engineering and data science teams. Why Join
Shape the foundation of ML infrastructure and tooling. High-impact role with visibility across teams. Competitive compensation and growth opportunities.
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Build and scale ML infrastructure and deployment pipelines. Empower data scientists with production-ready tools. Optimize model monitoring, retraining, and versioning. Job Information
Location:
Flexible / Remote Employment Type:
Full-Time Compensation:
$130,000 – $210,000 Role Summary
We are seeking an MLOps Engineer to design, implement, and maintain machine learning infrastructure at scale. You will build robust deployment pipelines, automate retraining workflows, and create tools that enable data scientists to efficiently deploy and manage models in production. This role combines engineering expertise with a deep understanding of ML lifecycle management. Key Responsibilities
Build and maintain CI/CD pipelines for machine learning workflows. Implement systems for model monitoring, logging, and performance tracking. Develop and manage feature stores for reliable ML data pipelines. Automate model retraining, versioning, and deployment. Collaborate with data scientists to streamline model deployment at scale. Ensure ML infrastructure reliability using Kubernetes and Terraform. Requirements
4–8 years of experience in MLOps, ML engineering, or related roles. Strong knowledge of CI/CD best practices for machine learning. Hands-on experience with Airflow, MLflow, Feast, and Kubernetes. Familiarity with infrastructure as code (Terraform). Proficiency in Python and ML deployment workflows. Proven ability to support large-scale ML systems in production. About the Opportunity
This role is ideal for someone passionate about building the backbone of machine learning systems. You’ll enable scalable, reliable, and automated ML deployments while collaborating closely with both engineering and data science teams. Why Join
Shape the foundation of ML infrastructure and tooling. High-impact role with visibility across teams. Competitive compensation and growth opportunities.
#J-18808-Ljbffr