Research and implement MLOps tools, frameworks and platforms for our Data Science projects.
Work on a backlog of activities to raise MLOps maturity in the organization.
Proactively introduce a modern, agile and automated approach to Data Science.
Conduct internal training and presentations about MLOps tools benefits and usage.
Required Experience And Qualifications
Wide experience with Kubernetes.
Experience in operationalization of Data Science projects (MLOps) using at least one of the popular frameworks or platforms (e.g. Kubeflow, AWS Sagemaker, Google AI Platform, Azure Machine Learning, DataRobot, DKube).
Good understanding of ML and AI concepts. Hands-on experience in ML model development.
Proficiency in Python used both for ML and automation tasks. Good knowledge of Bash and Unix command line toolkit.
Experience in CI/CD/CT pipelines implementation.
Experience with cloud platforms - preferably AWS - would be an advantage.
