• Created dashboard to view blood donation details for biotech Fortune 500 client with React, Javascript (TypeScript), NextJS, NodeJS (Express) and GraphQL with Kubernetes and CI/CD pipelines

• 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

• Cut loading time of internal government website by 83% by redesigning it in Typescript, Gatsby, React, and NodeJS.

• 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.

• Enabled a 50% increase in data science speed by developing a machine learning model for differential privacy using Python, Tensorflow, Keras, Docker, and hosted on Google Cloud.

• 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.

• Increased customer profits by 30% by building a custom machine learning based product repricer in PHP that learned from time of day, purchase type, and other factors, then integrated this into React as a full stack application and optimized page load speed to less than 100ms.

• 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.