MLOps Engineer

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

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

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