MLOps Engineer ID72409

AgileEngine, LLC; United States; On-site; USD 100,000 - 130,000; Full time

Job summary

AgileEngine, LLC is seeking a Middle/Senior MLOps Engineer to lead the transition from AI/ML experimentation to reliable production deployment. This role involves building and maintaining the infrastructure, automation, and workflows needed for efficient model deployment at scale.

You will implement production monitoring systems, manage GPU compute resources, and collaborate closely with data scientists to translate experimental models into production-ready solutions. This position is based onsite in Dallas, TX, requiring strong cloud management expertise and a good grasp of AI/ML fundamentals.

Qualifications

• Professional experience in MLOps, DevOps, Data Engineering, Machine Learning or Software Engineering.
• Hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring.
• Strong practical experience managing cloud environments and GPU resources.
• Deep understanding of AI/ML fundamentals.

Responsibilities

• Own the complete lifecycle transition from AI/ML experimentation to production deployment.
• Build and maintain infrastructure, pipelines, and automation for efficient model deployment.
• Implement robust production monitoring systems and data drift detection.
• Manage cloud environments and ensure cost-effective scalability.

Skills

MLOps

DevOps

Data Engineering

Machine Learning

Software Engineering

Containerization (Docker, Kubernetes)

Experiment tracking

Model versioning

Cloud environments

Production monitoring

Education

Degree in Computer Science, Software Engineering, or related field

Tools

Docker

Kubernetes

Job description

• What you will do

• 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.

• Must haves

• 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);
• Engineers located in the US must reside in Dallas, TX, and be willing to work onsite;
• 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;
• Upper-intermediate English level.

We are looking for a Middle/Senior MLOps Engineer to own the complete lifecycle transition from AI/ML experimentation to reliable production deployment, building and maintaining the infrastructure, pipelines, and automation needed to deploy models efficiently at scale. You will implement production monitoring systems, drift detection, experiment tracking, and model versioning, while managing cloud environments and GPU compute resources for cost-effective scalability. The role is based onsite in Dallas, TX, and requires close collaboration with data scientists and AI researchers to translate experimental models into production-ready solutions.