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
- 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;
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; manage cloud environments and GPU compute resources to ensure systems are not only highly scalable but also cost-effective.
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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