We understand that your time is valuable and want to thank you for working on this exercise.
A major aspect of the role is to design and implement the next generation of infrastructure and CI/CD for data science at Paidy. In this exercise you will be working to make the documentation for our internal Python package, PaidySuperAI, publicly available so that it's easy for our data scientists to review it while building the next generation ML models.
Please note, that anything you build as part of this assignment is entirely owned by you in perpetuity.
We might store/archive your solution and use it internally for discussion and evaluation.
For the source Python files in the subfolder src/, on any change within the source files,
the build pipeline (can be CircleCI, GitHub Actions, BitBucket pipelines, etc.) shall:
- Generate browsable documentation for PaidySuperAI (preferably using pydoc or Sphinx)
- Containerize the documentation and upload it to AWS ECR
- Deploy it on AWS and make it publicly accessible / browsable
Please see the requirements below for additional restrictions.
Please keep the following things in mind:
- Approach the exercise as if you are working in a real production environment.
- Document and explain any assumptions and decisions you make, and any shortcuts you take (for example, due to time constraints) in the process.
- Use AWS, Terraform, and Python where applicable.
- The documentation must be containerized.
- Since PaidySuperAI is an internal package we want to limit access to the documentation, blocking
all IPs outside the range
60.125.0.0/16and152.165.0.0/16. - Share the code, and documents you create or are going to present as part of this project with your interviewers. Version control solutions like GitHub, Gitlab, BitBucket, etc… are all acceptable). Please restrict public access to your submission.