Quant. My data pipelines measure in the billions of events/day. Left corporate around the time of the "Great Resignation of 2021" - Now I research and tinker on data pipelines and models to teach ai bots to trade: Tons of data, requiring millisecond level execution.. It's great. Offers less than 7 figures TC, don't waste either of our inbox space please.Past Life: IBM & The journey through Big Data / Data science (2012-2021)During my time at IBM, I went through various roles in Big Data, Analytics, and Data Science teams. By 2019, they were flying me around the world to banks and hospitals to setup ml data pipelines for training and scoring, before it was known as ai and inference. Towards the end I was the Watson Studio Hadoop Integration Tech Lead, where I played a leading role in building datalake integrations for various tools, e.g. Spark and TensorFlow, to enable private data model training and scoring.Passion Projects: Home Labs & ML Development 2021-now()Spearheaded the development of a robust 400+ pod k8s trading platform, achieving sub-150ms decision-making and execution across 500 markets. 400k live orders / day sent to the markets. In the ML learning path, you are told that "But you cant really beat the market:".. And some of us take that up as a challenge. Quant life you build it all from scratch which gives you a broad and interesting usecase to evaluate the hemorrhaging edge of open source tech. Technical Evangelism & ContributionsAvid Technical Evangelist with a deep-rooted passion for Data Science, Security, and Cryptocurrency. A pioneer in the Hadoop ecosystem, crafting "big data accelerators" at IBM back in 2012.Contributor to open-source projects like Jupyter, Apache Knox, Ambari, and various data science notebooks and tools, including the LLM TextGeneration WebUI.Presenter at dataworks 2018, Using kafka/spark/python to detect malicious actors on live blockchain data.With about three years in Healthcare IT/Software Design, I have a profound understanding of the importance of security.