Led 5 person team to create dashboard for a VC funded startup with React (NextJS), Node (NestJS), Javascript (TypeScript) hosted on Google Cloud using Cloud Run and Postgres

Developed full stack features for consumer ecommerce Fortune 500 client using microservices in Java / Kotlin and Spring Boot, with React, Javascript (TypeScript), React Query and NextJS for the frontend, hosted on Microsoft Azure using Azure VMs

Redesigned admin dashboard used by hundreds with React, Javascript (TypeScript), NodeJS, Apollo GraphQL for Fortune 500 client, hosted on AWS using Lambda, with Cloudwatch, DynamoDB, AppSync, and Docker

Led conversion of VC funded startup client app to Rust with GraphQL, Axum, WebSockets, Diesel ORM from NodeJS

Led a 3 person team to create a voice/video/chat-based telemedicine web app where patients could talk to therapists for a subscription fee, using React, Node, Javascript (TypeScript), Twilio API, hosted on AWS with DynamoDB, for startup client

Built a low-latency team productivity tool and chat application with Rust using Actix Web, Prisma ORM for backend REST endpoints and frontend in Flutter, Dart with over 1,600 people signed up

Spearheaded transition from hard-coded marketing pages to Contentful CMS with React and Javascript (TypeScript) with a Python (Django) backend

Built social network app for startup client with React Native frontend and Python (Django) backend

Contributed to a real estate technology client’s project for scanning and mapping rooms using a Ruby (Ruby on Rails) backend and React and Vite frontend

Created React / Redux dashboard for analysts to view and flag cybersecurity threats, using an automated threat model with machine learning. This model achieved 98% accuracy, built in Python, Tensorflow and Keras to combat internal and external cybersecurity threats for the Department of Defense.

Increased user efficiency by 50% for cybersecurity tasks by creating a chatbot that automatically loads in machine learning models and predicts threats using Python, Tensorflow, Keras, Docker and Kubernetes, deployed on AWS.

Automated government OS and application security (RedHat Enterprise Linux, Postgres, Nginx, etc.) using Ansible and Terraform to cut down manual technician time from 2 weeks to 1 hour.

Created AngularJS frontend for differential privacy model, which loads in under 100ms through frontend optimization.

Integrated CSVs with Postgres to allow seamless execution of SQL queries on CSV files on multiple separate AWS servers with Apache Spark and Hadoop (HDFS), decreasing time for execution from 8 hours to 30 minutes.

Increased Apache Hive and Impala query speed by 50% through meticulous analysis of performance metrics and bottlenecks.

Presented to the 30 member team on the usefulness of machine learning by demoing algorithms and use cases.

Cut down on-boarding of new customers from 10 days to 2 days by creating a custom Naive Bayes Classifier in PHP that automatically categorized Amazon products using machine learning with an accuracy of 90% over 10k products. Integrated product classifier into React as a full stack application.