Description
A model is not production-ready when training finishes. It still needs versioning, deployment, monitoring, rollback, retraining, and an answer for what happens when the incoming data changes.
Those lifecycle problems define this BelovTech MLOps position.
The engineer will build pipelines around deployment and retraining, operate Kubernetes-based ML infrastructure, implement drift and performance monitoring, maintain model registries, and reduce friction between data-science experiments and production services.
The stack may include MLflow, Kubernetes, Docker, Terraform, Python, CI/CD systems, feature stores, cloud platforms, and distributed-training tools.
This role suits infrastructure engineers who understand ML workflows or ML engineers who prefer platform reliability over model research.
Trainety has curated the listing for discovery. Verify the active job description directly with BelovTech.
https://www.belovtech.com/careers/mlops-engineer