Remote MLOps Engineer: Scale Production ML Pipelines

AgileEngine, LLC.; West Palm Beach (FL); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Growth opportunities

Competitive compensation

Remote work

Challenging projects

Collaborative culture

Well-being programs

Job summary

AgileEngine, LLC. is seeking a Middle/Senior MLOps Engineer to own the lifecycle from AI/ML experimentation to production deployment. You will build and scale infrastructure, CI/CD pipelines, and monitoring, while managing GPU resources for cost-effective scalability.

Collaborate closely with data scientists to translate experimental models into robust production solutions, with on-site work in Dallas, TX and a people-first, award-winning culture.

Qualifications

• 3+ years of professional experience in MLOps, DevOps, Data Engineering, Machine Learning, or Software Engineering.
• Degree in Computer Science, Software Engineering, or a related technical discipline (or equivalent practical experience).
• Authorized to work in the US; onsite in Dallas, TX with no visa sponsorship offered.

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, drift detection, and model versioning.
• Manage cloud environments and GPU resources for cost-effective scalability.
• Collaborate with data scientists to translate experiments into production-ready solutions.
• Ensure reproducibility and traceability of all models in production.

Skills

MLOps

DevOps

Data Engineering

Machine Learning

Software Engineering

Education

Bachelor's degree in Computer Science/Software Engineering

Tools

Docker

Kubernetes

CI/CD pipelines

Job description

AgileEngine, LLC. is seeking a Middle/Senior MLOps Engineer to own the lifecycle from AI/ML experimentation to production deployment. You will build and scale infrastructure, CI/CD pipelines, and monitoring, while managing GPU resources for cost-effective scalability.

Collaborate closely with data scientists to translate experimental models into robust production solutions, with on-site work in Dallas, TX and a people-first, award-winning culture.