Role OverviewThe MLOps Engineer will combine ML infrastructure, GPU optimization, and deployment across cloud and edge environments to move computer vision models from experimentation into reliable production.
What You Will Do
The role involves building reproducible pipelines, monitoring, and lifecycle controls for image and video workloads using Docker, Kubernetes, and CI/CD.
Why It Might Be a Fit
The ideal candidate will have hands-on experience with experiment tracking, model registry/versioning, drift detection, and production monitoring, as well as strong practical experience navigating cloud environments and managing/provisioning GPU compute resources.
Requirements
- Authorized to work for ANY employer in the US
- 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)
- Upper-intermediate English level
Benefits
- Professional growth
- Competitive compensation
- A selection of exciting projects
- Flextime
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