• Spearhead the design and implementation of end-to-end machine learning and data operations pipelines, resulting in 5 model endpoints served to over 40,000 users.• Increasing data ingestion by 500% by designing and implementing SQL data warehouses, and efficient data pipelines• Architect automated workflows for the training, evaluation and deployment of machine learning models in production environments. • Develop and fine-tune NLP algorithms, including LSTMs and transformers, implementing a real-time alert system for engineering teams on bug-related issues within the platform. • Integrate large language models to interpret student performance data dynamically, enhancing learning outcomes through adaptive model responses. • Leading company research initiative. Originally conducted research and proposed ten AI methodologies to enhance platform functionality, culminating in the acceptance of two projects.

Using LLMs with RAG architecture to streamline communication in B2C applications

• Lead efforts in automation of data delivery by architecting new ETL pipeline logic and managing team of data engineers. New pipeline increased efficiency in runtime over legacy pipeline by 250%. • Leveraged strong communication skills to address ad-hoc client requests, utilizing Python, SQL, and data transformations to revamp dashboards. Enhanced visibility of key performance indicators (KPIs) and streamlined reporting processes. • Built scripts to transform and join data to help case study dealing with 50% unattributed data points.

• Developed automation scripts for remote software installation, reducing manual setup time by 50% and enhancing operational efficiency. • Designed a data dissemination tool that aggregates data from APIs and remote databases, facilitating seamless integration into a local database for analysis. • Enhanced a signal intelligence device to optimize packet transmission to external applications, improving data communication and processing capabilities.

• Applied data visualization skills to create readable visuals in order to help users understand data.• Researched unsupervised machine learning algorithms pertaining to k-means clustering and use of PCA.• Manipulated large dataset to work with application.

• Checked quality of 80,000+ image dataset to assist in data pre-processing• Assisted in training new annotators and creating training documents• Lead team meetings

• Conducted research under a Machine Learning Engineer in order to increase working knowledge on machine learning algorithms specifically dealing with Neural Networks• Contributing to 80,000+ image data set to train machine learning algorithm in recognizing product details

Worked for RBDC to assist Lineage Logistics in point cloud classification project.• Studied new algorithms pertaining to point cloud classification.• Built neural network with Point Net and TensorFlow to classify point cloud data to help achieve a fully automated warehouse.• Correctly applied algorithms to guess point cloud data.

• Followed and modified set plans to maximize efficiency in project execution by 100%• Learned new skills pertaining to carpentry by listening and application• Major remodels including changing 1000+ doors campus wide, maintenance of ceiling and construction of shelving units