MLOps Engineer ID72409

AgileEngine•Orlando, FL

2d•Onsite

About The Position

AgileEngine is hiring a Middle/Senior MLOps Engineer to move computer vision models from experimentation into reliable production. The role combines ML infrastructure, GPU optimization, and deployment across cloud and edge environments. The engineer will build reproducible pipelines, monitoring, and lifecycle controls for image and video workloads using Docker, Kubernetes, and CI/CD. The position requires engineers located in the US to reside in Dallas, Texas, and work onsite. The role is strictly focused on computer vision and GPU engineering; candidates whose experience is heavily focused on LLMs, RAG pipelines, chatbots, or prompt engineering are not a fit without practical computer vision MLOps experience. Candidates must be authorized to work for any US employer; visa sponsorship or transfer is not available.

Requirements

At least 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.
Must be authorized to work for any employer in the United States; the employer cannot sponsor or take over employment visa sponsorship.
US-based engineers must reside in Dallas, Texas, and be willing to work onsite.
Hands-on experience with experiment tracking, model registry and versioning, drift detection, and production monitoring.
Strong hands-on experience with computer vision pipelines, including GPU model training, dataset management, and infrastructure monitoring specific to computer vision model quality and drift.
Practical experience navigating cloud environments and managing or provisioning GPU compute resources.
Deep understanding of containerization technologies such as Docker and Kubernetes, and designing robust CI/CD pipelines for automated deployments.
Conceptual understanding of AI/ML fundamentals sufficient to communicate, troubleshoot, and collaborate with applied model developers.
Upper-intermediate English level.
Practical computer vision MLOps experience; an experience profile focused heavily on LLMs, RAG pipelines, chatbots, or prompt engineering alone is not suitable.

Responsibilities

Own the complete lifecycle transition from AI/ML experimentation to reliable, high-performance production deployment.
Build, maintain, and scale infrastructure, automation, and CI/CD workflows for rapid and efficient model deployment.
Support large-scale image and video data ingestion, dataset versioning, and manual or automated image annotation workflows.
Implement production monitoring systems, visibility dashboards, and data and concept drift detection to support model accuracy and system reliability.
Manage experiment tracking and model versioning to ensure reproducibility and traceability of production models.
Collaborate with data scientists and AI researchers to turn experimental models into production-ready solutions.
Manage cloud environments and GPU compute resources for scalable, cost-effective systems.
Optimize models for heavy GPU workloads, including latency, throughput, batching, and GPU memory utilization.
Deploy computer vision models to cloud environments and optimize them for edge GPU deployment.