Led internal project of 5 engineers to build out ML solution use cases using Ezmeral Runtime Enterprise (ERE) MLOPs to enable the field, address knowledge and technical debts, and assist QA with product testing and quality whilst leveraging Jira and GitHubRepurposed solutions into 15+ demo videos with live commentary and PowerPoints to showcase end to end machine learning examples with frameworks such as Mlflow, Kubeflow, Katib, DeterminedAI, DVC, Spark to evaluate technical debts within the platform and educate field teams on machine learningExecuted in depth, technical efforts to explore competing vendors (AWS SageMaker and Azure Databricks) to enhance internal knowledge of the competitive landscape and understand product differentiation in the marketCurrent MLOps subject matter expert for all pre-sales and existing customer interactions

Developed machine learning development examples using Keras, RStudio, Spark, TensorFlow, and XGB to support the development and education of Ezmeral MLOps for global teams and customersConducted platform demos and MLOps consulting to over 20 presales, field, internal teams, and customers globally to showcase platform capabilities and enhancementsReproduced customer POC requirements on ERE and reported gaps in the product to the product team for further improvements to the

Coordinated web scrapers to accumulate data to expand company database using Python and Postgres. Researched and retrained legacy scikit-learn machine learning algorithms and reintroduced them back into production. Visualized database designs, NoSQL and relational, to support the creation of a new data acquisition tool.

Worked on researching the configuration and use of Ubiquiti energy meters, which dumps data into MongoDB for extraction. Led the development of data visualization efforts of electrical usage across 3 different sites in order to help managing directors understand the clients' usage patterns as part of the initiation of a mutli-year project.