You will be working in a data science project as an MLOps Engineer and you will:
Provide engineering (mlops, devops, cloud etc.) support to the project team
Implement cloud architectures as designed by our cloud architects
Maintain the project specific cloud accounts e.g. by including compliance requirements (cloud security findings), cost optimization, user access management etc.
Develop and maintain project CICD pipelines in github
Establish and maintain best practices (coding, cloud, devops, mlops) among the team
Build and maintain Kubeflow pipelines in cooperation with the Data Scientists working in the project
Must-have:
Cloud (AWS, Azure):
a. AWS (cloud architecting, engineering) >= 2 years
b. Azure (Cloud architecting, engineering) >= 1 year
c. Cloud security
d. Cost optimization
MLOps:
a. Kubeflow
b. Mlflow
Devops
a. Git
b. Github Actions for CICD
c. Docker
d. Kubernetes
e. IaC: Terraform, Cloudformation
Software engineering: > 3 years
Python: > 3 years
Databricks
Data Science: basic understanding
Machine learning frameworks:
a. Pytorch
b. Tensorflow
c. Scikit-learn