Remote MLOps Engineer — Scale Production Models

AgileEngine, LLC.; Fort Worth (TX); On-site; USD 130,000 - 190,000; Full time

Benefits offered by this job

Growth without limits

Competitive compensation

Flexibility

Meaningful, modern projects

Collaborative culture

Well-being & support

Job summary

AgileEngine is seeking a Middle/Senior MLOps Engineer to own the lifecycle from AI/ML experimentation to production deployment, building infrastructure, pipelines, and automation for scalable models. The role is based onsite in Dallas, TX, with collaboration across data scientists and AI researchers.

You will implement production monitoring, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU resources for cost-effective scalability.

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 (or equivalent).
• US work authorization; Dallas, TX onsite work.

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 production monitoring systems, dashboards, and drift detection for model accuracy and reliability.
• Manage experiment tracking and model versioning for reproducibility and traceability in production.
• 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 Engineering

Cloud Platforms

Docker/Kubernetes

CI/CD Pipelines

AI/ML Fundamentals

Education

Bachelor's in Computer Science or related field

Tools

Docker

Kubernetes

CI/CD

Job description

AgileEngine is seeking a Middle/Senior MLOps Engineer to own the lifecycle from AI/ML experimentation to production deployment, building infrastructure, pipelines, and automation for scalable models. The role is based onsite in Dallas, TX, with collaboration across data scientists and AI researchers.

You will implement production monitoring, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU resources for cost-effective scalability.