Position: Software Engineer
Category: Python
English level: fluent
Years of experience: 3

I helped create a cloud service that is easy to use and cheaper than the competion (Azure cloud, AWS S3) for deploying and running of AI models.
Project: Make Deepstack AI server a cloud service (i.e Build Deepstack cloud)
Motivation: Reduce the financial and technical cost of running and deploying AI models or services in the cloud
Note: Deepstack is an open-source AI server that is used for running and deploying AI models. You can check it out on Docker-hub (deepquestai/deepstack)
Solution: When a customer asks us to create a Deepstack cluster from our web dashboard the frontend API talks with a provisioning app that create a deployment job in the provisioning database. We created a python script that is running in a virtual machine on Azure that checks the provisioning database every 30 seconds for a deployment job that is set to processing. If there is a deployment job that is set to processing, we take the deployment data from the provisioning database give feed it as environment variables to a provisioning runner API which makes use of Pulumi infrastructure as a code to create the virtual machine on Azure cloud and export the property of the VM which includes the VM IP address is saved in a text file which is then fed to another python script which accesses the VM via a python package called RemoteSSH. RemoteSSH then connects to the VM install docker pull Deepstack Server image from azure container registry when all this is completed the provisioning database is updated and the deployment job is active. When the frontend API then queries the provisioning API the customer then sees that the Deepstack clusters requested have been successfully created.
An opportunity to work in a diverse team, rich with people with great values.  An opportunity to express me and grow in the company. An opportunity to learn new things, improve my skills, learn about others' cultures and become a world-class developer.