Senior MLOps Engineer - Remote, High-Impact Deployments

AgileEngine; Allen (TX); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Growth without limits

Competitive compensation

Remote-friendly (flexible hours)

Meaningful projects

Collaborative culture

Well-being programs

Job summary

AgileEngine in Dallas, TX, is seeking a Middle/Senior MLOps Engineer to own the lifecycle from experimentation to production deployment. You will build and maintain infrastructure, pipelines, and automation for scalable model deployment, collaborating with data scientists and AI researchers to translate experiments into production-ready solutions.

The role requires hands-on experience with monitoring, drift detection, and model versioning, as well as managing cloud environments and GPU resources

Qualifications

• 3+ years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering.
• Degree in Computer Science, Software Engineering or related technical discipline.
• Hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring.

Responsibilities

• Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment.
• Build, maintain, and scale the infrastructure, automation, and CI/CD workflows for rapid model deployment.
• Implement robust production monitoring systems and dashboards to ensure model accuracy and reliability.
• Manage experiment tracking and model versioning for full reproducibility in production.
• Collaborate with data scientists to translate experimental models into production-ready solutions.
• Manage cloud environments and GPU compute resources for scalable, cost-effective systems.

Skills

MLOps

DevOps

Data Engineering

Machine Learning

Software Engineering

Education

CS or Software Eng degree

Tools

Docker

Kubernetes

Job description

AgileEngine in Dallas, TX, is seeking a Middle/Senior MLOps Engineer to own the lifecycle from experimentation to production deployment. You will build and maintain infrastructure, pipelines, and automation for scalable model deployment, collaborating with data scientists and AI researchers to translate experiments into production-ready solutions.

The role requires hands-on experience with monitoring, drift detection, and model versioning, as well as managing cloud environments and GPU resources