Remote MLOps Engineer: Scale ML Deployments and CI/CD

AgileEngine; Carrollton (TX); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Growth without limits

Competitive compensation

Flexibility: 100% remote with flexible

Meaningful, modern projects

Collaborative culture

Well-being & support

Job summary

AgileEngine is seeking a Middle/Senior MLOps Engineer to own the complete lifecycle from AI/ML experimentation to reliable production deployment in Dallas, TX. You will build and maintain the infrastructure, pipelines, and automation for fast, scalable model deployment, with production monitoring, drift detection, and model versioning.

Collaborate with data scientists to translate experiments into production-ready solutions.

Qualifications

• 3+ years of professional experience in MLOps, DevOps, Data Engineering, ML, or Software Engineering
• Degree in Computer Science, Software Engineering, or a related technical discipline
• US-based onsite role with Dallas, TX residency requirements

Responsibilities

• Own lifecycle transition from AI/ML experimentation to production deployment
• Build, maintain, and scale infrastructure, automation, and CI/CD workflows
• Implement production monitoring, dashboards, drift detection, and model versioning
• Manage experiment tracking and model registry for reproducibility
• Collaborate with data scientists and AI researchers to translate experiments into production-ready solutions
• Manage cloud environments and GPU resources for scalable, cost-effective systems

Skills

MLOps

DevOps

Data Eng

Machine Learning

CI/CD

Docker/Kubernetes

Cloud Environments

English (Upper-Intermediate)

Education

Bachelors in CS/Software Eng or equivalent

Tools

Docker

Kubernetes

Job description

AgileEngine is seeking a Middle/Senior MLOps Engineer to own the complete lifecycle from AI/ML experimentation to reliable production deployment in Dallas, TX. You will build and maintain the infrastructure, pipelines, and automation for fast, scalable model deployment, with production monitoring, drift detection, and model versioning.

Collaborate with data scientists to translate experiments into production-ready solutions.