🗒️ Summary

Work on deploying and monitoring of Superlinked services into multiple cloud providers and own the DevOps and MLOps parts of our stack while delivering highly scalable infrastructure for the future of vector computation.

👀 Sneak peek into the work you’ll do

Deployment service

Compile the Superlinked framework elements to IaaS code that can deploy, scale and monitor the services that are necessary to process millions of vectors at scale and recalculate embeddings when the configuration changes.

Own the production constraints

You will establish best practices and processes that will ensure that every developer works with constraints in place that makes creating production ready code as close to reality as possible.

Package Superlinked into the GCP/Databricks Marketplace

Some of our largest clients need Superlinked to run in their cloud platform account, instead of using our hosted version. You would fully own the process of packaging and configuring the cloud platform to offer something as close as possible to a 1-click install similar to this: https://cloud.google.com/marketplace/solution/click-to-deploy-images/redis.

Devops

Help form the basics of DevOps to ensure continuous integration and delivery across all branches of development.

📝 Your experience

You have worked with multiple cloud providers (AWS, GCP, Azure) and have written IaaS solutions before (Terraform). You have experience in Python and have worked with MLOps solutions in the past (MLFlow, Airflow, Weight&Biases, CometML). You are keen on establishing an automated pipeline (Github Actions, Coverage Reporting, Docker).

Please use the link below to learn more.

https://superlinked.notion.site/Senior-DevOps-Engineer-11d04639c88c44e7b5ba7f4a0ba2f50f?pvs=4