Grebeňová Kateřina, e-mail: email odstraněn

Napln prace

Develop a state-of-the-art data science and ML runtime stack in a multi-cloud environment.
Understand and use computer science fundamentals, including data structures, algorithms, computability and complexity and computer architecture.
Manage the infrastructure and pipelines needed to bring models and code into production.
Demonstrate end-to-end understanding of applications (including, but not limited to, the machine learning algorithms) being created.
Build algorithms based on statistical modelling procedures and build and maintain scalable machine learning solutions in production.
Apply machine learning algorithms and libraries.
Research and implement best practices to improve the existing machine learning infrastructure.
Collaborate with data engineers, application programmers and data scientists.

Kvalifikace

Qualification in some of the related fields such as computer science, statistics, electrical engineering, mathematics, or physical sciences.
Self-starter with excellent communication/time management skills.
Computer programming is a must, knowledge of Python.
Experience scaling machine learning on data and compute grids.
Proficiency with K8n, Docker, Linux and cloud computing.
Experience with MLflow.
MLOps, CI, Git and Agile processes.
English language communication skills