• 🔭 I’m currently solving Quality and Testing problems with AI at Qualiti 🐨
• 🌱 I lead QA at the Point(QAP), the Software Quality Community in Utah, USA
• 😎 Former Engineering Manager at Adobe
• 🧡 Software Tester at heart
• 🗣️ I do workshops and presentations all over the world!
• 📫 How to reach me: LinkedIn, Twitter/X or carlos@qap.dev
• ⚡ Fun fact: I play futsal every morning at 5:30 ⚽

I've done hands-on workshops, courses, breakout sessions, keynotes, and more all over the world for the past 10 years. Here are some examples:

🎉 I will be keynoting at DevOpsDays SLC in 2024!

• Presentation: Testing Machine Learning Models at PyCon US (2022)
• Presentation: Future of Test Data Generation with AI at TestFlix (online 2022)
• Presentation: Devs should love your tests at Selenium Conference (London 2019)
• Course: Intro to Testing ML Models at Test Automation University
• Course: Scaling Tests with Docker at Test Automation University
• Guest: How to get automation included in your Definition of Done by Angie Jones at QAP
• Guest: Building AI Systems that work and understand those that fail by Ben Taylor at QAP

As you can imagine, with over a decade of very hands-on experience, I have worked deeply with many tools and technologies. I provide many examples below, but I didn't want to duplicate multiple icons like AWS even though I have used it in every group you'll see. There isn't a pretty icon for everything I use, like playwright and langchain, but this should give a good idea of what I know well.

🤔 I don't include anything that I don't know well enough, like React and GraphQL, even though I have written React before. Keeping myself honest

Python is my primary language since I mostly deal with data, automation, and AI.

AWS, and its services, have been the main "cloud" I've used throughout my career and I have worked with many databases.

I have worked with a vast amount of tools in this space for many types of testing: UI, API, Security, A11y, O11y, Performance, etc.

Early in my journey, I had to build my own datasets and train models from scratch. However, the last year has shown how LLMs have made such a huge impact!

I have built multiple features using closed-models, like OpenAI's GPT-4, but I have also used open-sourced models from HuggingFace and even Mixtral!

• LangChain has been my main framework for creating the pipelines and switching models for each step
• LangSmith for observability
• LangServe for deployments
• I have built things using just Python as well, but LangChain is getting better and better!

You can see my 6 Pinned Projects below, but you can also see all the repos I've created for the QA at the Point (QAP) Community!

My latest project was the QAP Job Poster which scrapes jobs from different sources, filters and ranks them using AI, then posts them to the #jobs channel in our Slack group.