Senior MLOps Engineer - Remote, Flexible Hours & Growth

AgileEngine; Blacksburg (VA); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Growth without limits

Competitive compensation

Flexibility

Meaningful projects

Collaborative culture

Well-being support

Job summary

AgileEngine, an Inc. 5000 company, seeks a Middle/Senior MLOps Engineer to own end-to-end production deployment of AI/ML models and scalable infrastructure. You will implement monitoring, experiment tracking, and drift detection while collaborating with data scientists to translate experiments into production-ready solutions.

The role is based onsite in Dallas, TX, requiring strong cloud, GPU, and CI/CD expertise to deliver cost-effective, high-performance deployments.

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-based onsite role with Dallas, TX residency and authorization to work in the US.

Responsibilities

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

Skills

MLOps

DevOps

Data Engineering

Machine Learning

Software Engineering

Education

B.S. in Computer Science or related

Tools

Docker

Kubernetes

Cloud platforms

CI/CD

Job description

AgileEngine, an Inc. 5000 company, seeks a Middle/Senior MLOps Engineer to own end-to-end production deployment of AI/ML models and scalable infrastructure. You will implement monitoring, experiment tracking, and drift detection while collaborating with data scientists to translate experiments into production-ready solutions.

The role is based onsite in Dallas, TX, requiring strong cloud, GPU, and CI/CD expertise to deliver cost-effective, high-performance deployments.