Eric Waters

Hi, I'm Eric

I build powerful backend systems and intuitive interfaces — always with clean, optimized code and a focus on impact.

Intel Corporation
Software Application Development EngineerAugust 2023 - PresentCreated an AI contract auditing tool saving $30M, a modular Generative AI web platform, a no-code AI app studio, a scalable ML analytics API, and administered an AI search platform.
Arizona State University
Computer Science (Big Data Systems), M.S.August 2022 - May 2023Studied Data Mining, Data Processing at Scale, Statistical Machine Learning, Data Visualization, Database Management System Implementation, and Mobile Computing.
Dell Technologies
Graduate Software Engineering InternJune 2022 - August 2022Implemented OAuth 2.0 SSO and fine-grained authorization across enterprise apps and APIs, unifying authentication and enhancing security and compliance.
Arizona State University
Computer Science, B.S.August 2019 - May 2022Studied Data Structures and Algorithms, Programming Languages, Operating Systems, Software Engineering, Database Management Systems, QA & Testing, and various mathematics.
Dell Technologies
Undergraduate Software Engineering InternMay 2021 - August 2021Developed a Kanban board app with Spring/MySQL to enhance team agility, and won 1st place in Dell's Intern Hackathon with an Adobe XD onboarding prototype.

Quality Code, Real Results

Over 2 years of professionally building and shipping full-stack applications.

2023 - Present

Intel Corporation

Software Application Development Engineer

Developed an AI-powered contract auditing tool that enabled enterprise legal teams to identify risks and inefficiencies, resulting in up to $30M in estimated cost savings.

Built a modular web platform for Generative AI tools using Angular and Sanic, enabling rapid development of AI-powered apps with reusable components, unified UI, and robust API integrations.

Designed a no-code AI app studio using React, Next.js, and Tailwind, empowering teams to create and deploy AI apps without using development resources.

Engineered a scalable ML analytics API for manufacturing data using FastAPI, Docker, and Kubernetes, enabling seamless integration of advanced analytics into multiple applications.

Independently administered an AI search platform for manufacturing data, enabling faster problem-solving. Created robust ETL pipelines using Pandas and SQL Server to ingest, normalize, and consolidate data across business units.

2022

Dell Technologies

Graduate Software Engineering Intern

Implemented OAuth 2.0 SSO across several enterprise apps, unifying authentication and enhancing user experience.

Integrated fine-grained authorization controls for several web apps and REST APIs using Spring Boot and Angular, strengthening security and compliance.

2021

Dell Technologies

Undergraduate Software Engineering Intern

Built a custom Kanban board app with Spring and MySQL, improving agile collaboration and task tracking for engineering teams.

Designed an interactive Adobe XD prototype to streamline the onboarding experience for an employee resource group, earning 1st place in the Dell Intern Hackathon.

Academic Excellence

Rigorous study in computer science and engineering principles.

Computer Science, M.S.

Big Data Systems ConcentrationArizona State University2022 - 20234.0 GPA

Overview

Focused on designing and deploying scalable systems for processing and analyzing large-scale, complex datasets. Gained hands-on experience with distributed computing, real-time data processing, data mining, machine learning, and visualization through project-based learning—equipping me to develop efficient, data-driven solutions across various domains.

Key Coursework

Data Processing at Scale
Data Mining
Semantic Web Mining
Statistical Machine Learning
Data Visualization
Knowledge Representation
Mobile Computing
Database Management System Implementation

Automated Detection of Phishing Attacks using Machine Learning Techniques

A Data Processing Pipeline with Kafka, Neo4j, and Kubernetes for Distributed Graph Algorithm Processing

Statistical Machine Learning Approaches in Medicine and Biomedical Sciences

Fiscal Clarity: Turning Data into Insights for Engagement, Ohio

Stance Detection of Tweets

Solving the Automated Warehouse Scenario using Answer Set Programming

Computer Science, B.S.

Arizona State University2019 - 20224.0 GPA

Overview

Studied the design, development, and analysis of software systems, building a strong foundation in programming, data structures, algorithms, and computer systems. Gained hands-on experience in machine learning, cybersecurity, and software engineering, developing the skills to solve complex technical problems and build robust, scalable applications.

Key Coursework

Programming Languages
Object-Oriented Programming & Data Structures
Data Structures & Algorithms
Database Management
Machine Learning
Software Engineering
Operating Systems
Information Assurance
Software QA & Testing
Probability & Statistics Engineering Problem Solving
Theoretical Computer Science

Undergraduate Thesis:
Party on Wall Street

Undergraduate Capstone:
Developing a Contractor Management System

A Powerful Toolbox

Robust, relevant, and always evolving.

Programming Languages

Typescript

A superset of JavaScript that adds static typing. I use it in all my web projects, as it helps catch errors early and improves code quality.

Python

A versatile language that I use extensively for data analysis, web APIs, machine learning, and one-off automation scripts.

Java

A high-level OOP language that was my first. I used it throughout high school, college, and in my internships to build everything from command line tools to web applications.

C/C++

A low-level language that I used in multiple college courses for learning principles of programming languages, data structures, and algorithms.

Microsoft's object-oriented language. I've used it for Unity game development and an Intel desktop application.

SQL

A language for managing and querying databases. I studied it formally in several database courses and use it very frequently in my work as an owner of several relational databases.

Web Development

React

A JavaScript library for building user interfaces. I've built multiple applications using React, including this website. It's my go-to library for web development.

Angular

A comprehensive framework for building web applications. I've used it extensively at both Intel and Dell. I appreciate its batteries-included approach and dependency injection system.

Tailwind CSS

A utility-first CSS framework for rapidly building custom user interfaces. I use it in most of my web projects, including this website. It drastically improves my productivity and design consistency.

Next.js

A React framework for building full-stack web applications. I've used it for multiple projects, including this website. I love its performance, file-based routing, and server actions.

Spring

A Java-based framework for building enterprise applications. I used it in both my Dell internships to build web applications and REST APIs.

Laravel

A PHP framework for building web applications. I used it for a year-long capstone project where I built a full-stack contractor management system.

FastAPI

A modern web framework for building APIs with Python. I used it at Intel to build a high-performance API for running machine learning analysis jobs.

Sanic

A Python web framework optimized for speed and asynchronous APIs. At Intel, I used Sanic to build scalable, low-latency endpoints for serving generative AI models in production.

OAuth 2.0

An open standard for access delegation. I've used it in multiple projects to implement secure authentication and authorization flows.

D3.js

A JavaScript library for producing dynamic, interactive data visualizations in web browsers. I learned it formally in a graduate data visualization course and built many highly interactive custom visualizations.

Data Technologies

Pandas

A powerful Python library for data manipulation and analysis. I used it extensively in graduate school and at Intel for data cleaning, transformation, and analysis tasks.

NumPy

A fundamental package for scientific computing with Python. I used it in graduate school for numerical computations and data manipulation, especially in conjunction with Pandas.

Matplotlib

A plotting library for Python. I used it in graduate school for data visualization tasks, creating static, animated, and interactive visualizations in Python.

Kafka

A distributed event streaming platform. I used it in a graduate school project as the foundation of a data pipeline to stream taxi trip data into Neo4j.

Spark

A distributed data processing engine. I used it in a graduate school project to build a data pipeline for batch processing of large-scale datasets stored in Hadoop.

Hadoop

A framework for distributed storage and processing. I used it in a graduate school project to implement MapReduce algorithms for processing large distributed datasets.

Neo4j

A graph database management system. I used it in a graduate school project to model taxi trip data and apply the PageRank algorithm to identify high-traffic routes.

SQL Server

Microsoft's enterprise relational database. At Intel, I managed and analyzed millions of records, using its security, high availability, and Microsoft integration features.

PostgreSQL

A leading open-source relational database. I used it in academic and professional projects for structured data, leveraging its lightweight setup and JSON support.

MySQL

A lightweight and simple relational database. I've used it in many personal projects and at Dell for building web applications, appreciating its ease of setup and efficient performance.

ML/AI Tools

TensorFlow

A platform for building and deploying ML models. I used it extensively in graduate school for various ML tasks and leveraged TensorFlow Lite to deploy models on mobile devices.

LangChain

A framework for building applications with LLMs. I used it at Intel to develop APIs to serve generative AI models for creating chatbots and other AI applications.

Scikit-learn

A Python library for machine learning. I used it in graduate school for various ML tasks, including classification, regression, and clustering.

Keras

A high-level neural networks API. I used it in graduate school for building and training deep learning models, especially for image classification tasks.

DevOps & Cloud

Docker

A platform for developing, shipping, and running applications in containers. I've studied it academically and used it professionally to ensure consistent environments across development, testing, and production.

Kubernetes

An open-source system for automating deployment, scaling, and management of containerized applications. I've studied it in graudate school and used it at Intel to orchestrate containerized applications in production environments.

GitHub Actions

A CI/CD tool that allows you to automate your software workflows. At Intel, I built my own workflows from scratch to automate testing, deployment, and other tasks in several projects.

Rancher

An open-source platform for managing Kubernetes clusters. I've used it at Intel to deploy and manage containerized applications across multiple clusters.

Harbor

An open-source cloud-native registry that stores, signs, and scans content. I've used it at Intel to manage and secure container images in my projects.