product + design + engineering

As a product driven software engineer in Artificial Intelligence, you'll typically find me using the following tools as they're great for building products end to end:

• Dynamic, interpreted, programming languages such as python and javascript to build the backend and frontend.
• Static, compiled, programming languages for heavy lifting such as golang or those that run on the JVM, java.
• Markup languages for presenting and styling information and content such as html, css with sass, and jsx for React.
• Version control systems like git and hg hosted on GitHub, GitLab or Atlassian to work collaboratively, ideally using trunk based development.
• Miscellaneous operational tools and services such as docker to contain services, kubernetes for orchestrating them, CircleCI for building CI and CD pipelines, and aws to put services into production.
• Data exploration tools such as juypter and ipython paired with numpy and other tools to figure out if things are correct or to gain new insights.

If you don't see what your team is using, don't worry! I use the right tools for the job and I'm not tied to any particular stack. Personally, I believe it's really important to build things with sound engineering principles regardless of the stack that also makes sense for the business.