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# REMOTE MLOps Engineer

- **Company:** Insight Global
- **Location:** Plano, TX, United States
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Bash Shell, Command-Line Interface, Software Quality, Continuous Integration, DevOps, Programming Tools, Document-Oriented Databases, Python (Programming Language), Machine Learning, Software Engineering, Software Systems, SQL Databases, Test-Driven Development (TDD), Git, Pandas, Kubernetes, Deployment Automation, Machine Learning Operations, Software Version Control, Docker, Golang
- **Published:** August 23, 2026
- **Apply:** https://www.techcareers.com/job.asp?id=3363350305&tx=FJ4542FFF&pt=1&aff=0B19D771-A501-4A5E-8338-2A822B784D54&utm_source=Job%20Feed&utm_medium=textkernel&utm_campaign=DE&utm_term=0B19D771-A501-4A5E-8338-2A822B784D54

## About the Role

5+ years of experience in Software Engineering, DevOps, MLOps, or a related field

 - Experience delivering and supporting production-grade software systems

 - Strong proficiency in Python, SQL, Pandas

 - Hands-on experience with Docker, Kubernetes (self-hosted environments preferred)

 - Git and modern version control workflows, CI/CD pipelines and deployment automation

 - Strong command-line (CLI) proficiency

 - Experience gathering technical requirements and translating them into implementation plans

 - Demonstrated experience with Test-Driven Development (TDD)

 - Ability to independently develop and run end-to-end prototypes in a local containerized environment - Golang

 - Bash scripting

 - Experience with Kubeflow

 - Experience using AI-powered development tools for coding and software delivery

## Description

We are seeking an experienced Senior MLOps Engineer to support a high-impact initiative focused on modernizing machine learning infrastructure. This role will play a critical part in migrating existing machine learning pipelines to a modern Kubeflow-based architecture, helping accelerate the adoption of scalable, production-ready MLOps practices.

The ideal candidate will bridge the gap between data science and engineering, ensuring machine learning workflows are reliable, maintainable, and optimized for production. This is a hands-on role for someone who enjoys building infrastructure, improving development practices, and enabling data scientists to move faster.

About the Team

The E-Commerce MLOps team provides machine learning engineers and data scientists with a fully featured platform for research, development, and deployment of ML solutions.

This initiative is focused on accelerating workload migrations and achieving key platform goals before year-end.

What You'll Do

 - Analyze and document data input/output requirements for existing ML pipelines

 - Break down existing workflows into modular, self-contained pipeline components

 - Migrate machine learning pipelines to a Kubeflow-based platform

 - Develop and maintain containerized solutions using Docker and Kubernetes

 - Follow Test-Driven Development (TDD) methodologies to ensure code quality and reliability

 - Leverage AI-assisted development tools such as Codex, Claude, and Windsurf to increase productivity

 - Build, test, and validate end-to-end prototypes locally before deploying to production environments

 - Collaborate closely with machine learning engineers, data scientists, and platform teams

 - Contribute to CI/CD processes, automation, and deployment best practices

 - Help establish scalable engineering standards for machine learning operations


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