MLOps Engineer

Elios Talent • Austin, TX, US

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Overview

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.

Location : Flexible / Remote

Employment Type : Full-Time

Compensation : $130,000 – $210,000

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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Mlops Engineer • Austin, TX, US