MLOps Engineer ID72409

AgileEngine•Downey, CA

1d•Hybrid

About The Position

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.

Requirements

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
Upper-intermediate English level

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