MLOps engineer

Requirements:
2+ years of experience in MLOps/ML.
4+ years of experience in DevOps.
Strong knowledge of Python and SQL.
Experience with ML libraries (TensorFlow, PyTorch, NumPy etc.).
Experience in machine learning model deployments and pipelines.
Experience with MLOps frameworks/tools (e.g. Sagemaker pipelines/ Azure ML Studio/ VertexAI/ Kubeflow/ MLFlow).
Hands-on experience with Cloud Services(AWS, GCP, Azure).
Experience with Docker, Kubernetes, CI/CD, IaC, Prometheus, Grafana.
Understanding of data integration, and database management.
Quick learning abilities.
English upper-intermediate оr higher.

Responsibilities:
Develop, refine, and use ML engineering platforms and components, development workflow pipelines.
Deployment of open-source and other models to different instances.
Collaborate with ML architect and data scientists to curate high-quality datasets and optimize data workflows.
Rapid model deployment implementation.
Developing process-related documentation.
Kubeflow and MLFlow upgrade.