WebSockets

The candidate summary page summarized a candidate's performance during multiple interview stages by presenting radar charts showing degree of fit against the values and attributes being evaluated for their position, as defined in the Interview Builder.

I built the frontend in React and Typescript, and integrated with the backend, which I partially built, which leveraged RAG and ran several Machine Learning models to produce scores and explainable AI. For example, models to break down interview transcripts into quotable fragments, evaluate relevance against configured company values, and call chatGPT APIs to obtain summaries and scores related to that content

Instead of splitting devops and infrastructure and tests completely separate from development teams, I moved the needle so that product development teams could own more of their own infrastructure and tests, creating less back-and-forth and empowering teams to deliver.

We used Devspace, which meant any dev or team could stand up a reproducible, isolated stack with multiple services and frontends running, in the cloud, as well as modify the definitions of the infrastructure and code themselves, directly, without permission or external team tickets.

This enabled product engineers to do more experimentation and testing thru declarative infrastructure and configuration management while still protecting our production environments, unlocking their shackles and potential as the experts in the software.

At the same time I worked to reduce the outsized role our amazing DevOps team was playing in the day to day management as well as enhancement of environments, which unfairly impeded expert developers by introducing red tape and inter-team processes that didn't add value.