Hello There 👋!
I am Tom Everson
Full-Stack SWE and DS student specializing in AI/ML development, Backend systems, and Data Pipeline. Experience spans from software engineering at Agoda to AI development at Innovera, with additional roles at Lillup and ConceptX.
Summer 2025
Software Engineering Intern
- Engineered automated change-tracking features for a high-throughput audit management system processing 200+ daily marketing campaigns, enhancing data integrity monitoring by 40%
- Enhanced monitoring dashboards in Grafana for the audit pipeline, reducing system MTTR by 77% (from 2 hours to 45 minutes)
- Developed 20+ end-to-end test suites using Cypress, and ScalaTest, achieving 90% test coverage and reducing production bugs by 20%
Spring 2025
AI Engineer Intern
- Architected and deployed a production-ready RAG system on AWS for technical document retrieval, improving information retrieval accuracy by 25% across 300+ enterprise documents
- Optimized semantic search pipeline using Pinecone and OpenAI embeddings, reducing query latency by 41% (from 6.8s to 4.0s) and achieving 85% relevance scores
- Engineered cloud infrastructure using AWS Lambda, SageMaker, and S3, ensuring 99.2% uptime while serving 150+ daily queries
Summer 2024
Backend Intern
- Deployed backend infrastructure for 8 microservices using Docker and Cloud Run on Google Cloud, leveraging CI/CD workflows with CircleCI, achieving 99.9% uptime and supporting 1,000+ active users
- Migrated the CI/CD pipeline from Jenkins to CircleCI, automating deployment across testing, staging, and production environments, reducing deployment errors by 30% and improving deployment speed by 40%.
- Integrated Java Spring applications with GCP services, enhancing microservice communication and reducing service downtime by 30%, while improving system scalability and fault tolerance in distributed cloud architectures.
2023-2024
Frontend Developer
- Migrated Single Page Application from Vue and Webpack to a Server-Side Rendered Application using Nuxt and Vite, improving page load speed, SEO performance, user experience, and development speed by 40%.
- Cut initial page load time by 35% by implementing advanced frontend performance techniques, including lazy-loading and code splitting
- Designed and implemented a real-time notification system, leveraging WebSockets and push notifications to improve user engagement and ensure timely updates, increasing user retention by 15%.
Built a machine learning model using XGBoost to analyze and optimize discount rates from Amazon. The model, trained on historical sales data, identifies optimal discount levels to maximize revenue. The strategy was validated with an A/B test.
Built a machine learning model using XGBoost to predict the sales of the coffee. I also performed exploratory data analysis (EDA) on the data to understand key trends and relationships
Newsletter Platform For Creative
Space Hosting and Finding Platform For Creative
Facebook Clone Written in C using Socket and No Dependecies
A minimalistic programming language focused on simplicity and efficiency, featuring a compact, dependency-free implementation and supporting three execution modes: Interpreter, Transpiler, and Compiler
AI-powered Groceries detector using computer vision and image detection