Remote MLOps Engineer - Scale Production AI

AgileEngine, LLC.; Miami (FL); On-site; USD 140,000 - 180,000; Full time

Benefits offered by this job

Growth without limits

Competitive compensation

Flexibility

Meaningful, modern projects

Collaborative culture

Well-being & support

Job summary

AgileEngine, LLC. in Dallas, TX seeks a Middle/Senior MLOps Engineer to own the lifecycle from experimentation to production deployment. You will build and maintain the infrastructure, pipelines, and automation needed to deploy models efficiently at scale.

You will implement production monitoring, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU resources for cost-effective scalability. Onsite collaboration with data scientists is essential.

Qualifications

• Authorized to work in the US without sponsorship.
• 3+ years of professional MLOps/DevOps/ML/SE experience.
• Degree in Computer Science or related field.
• US Dallas, TX onsite required.

Responsibilities

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

Skills

ML Ops experience

AI/ML fundamentals

Experiment tracking

Model registry

Drift detection

Production monitoring

CI/CD pipelines

Cloud environments

Education

Bachelor's degree in Computer Science / Software Engineering

Tools

Docker

Kubernetes

CI/CD tooling

GPU compute

Cloud platforms

Job description

AgileEngine, LLC. in Dallas, TX seeks a Middle/Senior MLOps Engineer to own the lifecycle from experimentation to production deployment. You will build and maintain the infrastructure, pipelines, and automation needed to deploy models efficiently at scale.

You will implement production monitoring, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU resources for cost-effective scalability. Onsite collaboration with data scientists is essential.