Senior MLOps Engineer - Production CV/GPU Pipelines

AgileEngine; Austin (TX); On-site; USD 140,000 - 190,000; Full time

Benefits offered by this job

Professional growth

Competitive compensation

A selection of exciting projects

Flextime

Job summary

AgileEngine is seeking a Middle/Senior MLOps Engineer to move computer vision models from experimentation into reliable production. You will build reproducible pipelines, monitoring, and lifecycle controls for image and video workloads using Docker, Kubernetes, and CI/CD.

You will own end-to-end deployment, optimize GPU resources, and collaborate closely with data scientists to translate experiments into robust solutions for cloud and edge deployments.

Qualifications

• 3+ years of professional MLOps, DevOps, Data Engineering, ML, or Software Engineering.
• Degree in Computer Science, Software Engineering, or related discipline (or equivalent).
• US-based engineers must reside in Dallas, TX and be onsite.
• Roles focused on Computer Vision and GPU engineering with CV MLOps experience.

Responsibilities

• Own end-to-end deployment of vision models from experiments to production.
• Build and scale infra, pipelines, and CI/CD for GPU workloads.
• Implement production monitoring and drift detection for CV models.
• Manage experiment tracking and model versioning for reproducibility.
• Collaborate with data scientists to productionize CV solutions.
• Optimize cloud and GPU resources for cost-efficiency.
• Deploy CV models to cloud and edge environments.

Skills

MLOps

DevOps

Data Engineering

Machine Learning

Software Engineering

Experiment Tracking

Model Registry

GPU Engineering

CV Pipelines

Cloud Environments

Education

Bachelor's degree in Computer Science or related field

Tools

Docker

Kubernetes

CI/CD pipelines

Job description

AgileEngine is seeking a Middle/Senior MLOps Engineer to move computer vision models from experimentation into reliable production. You will build reproducible pipelines, monitoring, and lifecycle controls for image and video workloads using Docker, Kubernetes, and CI/CD.

You will own end-to-end deployment, optimize GPU resources, and collaborate closely with data scientists to translate experiments into robust solutions for cloud and edge deployments.