Hire Paul G. - Generative AI Architect

About paul

I'm passionate about crafting productivity-enhancing tools for software engineers. My professional journey began at Micron in 2000, where I contributed to baserv, a Perl and C++ framework, serving as a foundation for Micron's global manufacturing execution system. I then served as a core team member creating a common platform for UI and Business Objects using C# and WPF, ensuring consistent interfaces and shared logic across manufacturing applications. In 2015, I transitioned my focus to data warehousing and big data. I quickly came to rely on Apache NiFi to help manage the Hadoop herd. In 2017, I led a team of engineers in Taiwan to create Pipeline Builder - a NiFi backbone that was automated via REST calls from a Java service. An Angular frontend provided the UI collecting information about source, destination and loading strategies. This was used to deploy thousands of concurrent data feeds into the data warehouse. In 2018, I orchestrated the migration of 5000+ data pipelines from Micron's legacy warehouse to Snowflake. During this project, I developed reusable patterns called 'Snow Machine' and 'Snow Cat'. The Github repository for these can be found at https://github.com/paulgibeault/SnowflakeNiFiProcessors. Transitioning to a direct leadership role in 2019, I formed an international team of senior engineers in US, Taiwan and India. Our initial creation was the Global Data Middle Layer (GDML) addressing Micron's intricate manufacturing data landscape. GDML simplified data access through REST, incorporating robust authentication and authorization. SMAPI (Smart API), an extension of Python's FastAPI, emerged from GDML, streamlining API development and fostering consistency. Now the fire of GenAI is upon us and I am smitten. As an engineer, I am fascinated with tools that can boost productivity. The capabilities of LLM models and Generative AI concepts, while not consumer-grade products yet, provide a powerful base to build new tools that dramatically increase the potential of everyone. Recently, I have been working with my team to create a unified orchestration and data access layer, simplifying and standardizing the task of creating GenAI agents. This framework is in production providing Search+Q&A using a configurable set of processing steps.

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