Role OverviewWe 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.
What You Will Do
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.
Why It Might Be a Fit
You will partner closely with data scientists and AI researchers to translate experimental models into robust, production-ready solutions, and have the opportunity to work on meaningful, modern projects with global teams and leading brands.
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)
- 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
Benefits
- Growth without limits
- Competitive compensation
- Flexibility
- Meaningful, modern projects
- Collaborative culture
- Well-being & support
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