Senior MLOps Engineer: Scalable AI Production & DevOps

AgileEngine; Dallas (TX); On-site; USD 120,000 - 180,000; Full time

Benefits offered by this job

Professional growth

Competitive USD-based compensation

Exciting projects

Flextime

Job summary

AgileEngine in Dallas, Texas is looking for a Middle/Senior MLOps Engineer to own the lifecycle from AI/ML experimentation to reliable production deployment. You will build and maintain infrastructure, pipelines, and automation to deploy models at scale and monitor performance.

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

Qualifications

• 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).
• Hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring.
• Strong practical experience navigating cloud environments and managing/provisioning GPU compute resources.
• Deep understanding of containerization (e.g., Docker, Kubernetes) and designing robust CI/CD pipelines for automated deployments.
• A solid conceptual understanding of AI/ML fundamentals to effectively communicate, troubleshoot, and collaborate with applied model developers.

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 necessary for rapid and efficient model deployment.
• Implement robust production monitoring systems, build visibility dashboards, and set up data and concept drift detection to ensure ongoing model accuracy and system reliability.
• Manage experiment tracking and model versioning to ensure full reproducibility and traceability of all models in production.
• Partner closely with data scientists and AI researchers to translate experimental models into robust, production‑ready solutions.
• Manage cloud environments and GPU compute resources to ensure systems are not only highly scalable but also cost‑effective.

Skills

MLOps

DevOps

Data Engineering

Software Engineering

Machine Learning

Education

Degree in Computer Science or related field

Tools

Docker

Kubernetes

CI/CD

Experiment Tracking

Model Registry/Versioning

Job description

AgileEngine in Dallas, Texas is looking for a Middle/Senior MLOps Engineer to own the lifecycle from AI/ML experimentation to reliable production deployment. You will build and maintain infrastructure, pipelines, and automation to deploy models at scale and monitor performance.

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