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Hi! My Name is

Parth Ahuja

Welcome to my website! I'm a Software Engineer currently working at Intuit on AI agents and Payroll workflow automations. I've previously worked at Labcorp, Citrix, and Willis Towers Watson. I graduated from Purdue University in 2023.

Experience

Oct 2025 – Present

Software Engineer 2

Intuit

  • ›Build and scale QuickBooks AI Agent experiences within Intuit Intelligence, enabling employers and employees to automate complex workflows across payroll processing, issue resolution, and task execution.
  • ›Architect and develop a skills-based agent framework that allows AI agents to dynamically compose reusable capabilities across Payroll, Invoicing, Business Intelligence, and other QuickBooks domains.
  • ›Design production-grade agent systems using Python, LangChain, and Kubernetes, supporting reliable orchestration of multi-step workflows, tool execution, and domain-specific business logic.
  • ›Drive the evolution of QuickBooks Payroll toward a more real-time, agent-driven architecture, improving responsiveness and enabling richer in-product AI interactions.
  • ›Fine-tune and evaluate custom models on payroll-specific data and legacy monolith rules, improving accuracy for rule-heavy payroll decisions and workflow execution.
Python
LangChain
Kubernetes
AWS
AI/ML
Jan 2024 – Oct 2025

Software Engineer

LabCorp

  • ›Migrated legacy authentication systems to Okta SSO for 10,000+ users, strengthening identity security and streamlining enterprise login flows.
  • ›Built a scalable .NET API that automated order confirmation emails for DNA test results, reducing manual email operations by 95%.
  • ›Modernized critical services with Docker, OpenShift, and IBM API Connect, improving deployment speed and standardizing API delivery.
  • ›Integrated automated WCAG-based validation into Jenkins pipelines, improving accessibility compliance by 80% and reducing manual QA effort.
.NET
Okta
Docker
OpenShift
Jenkins
Python
May 2023 – Aug 2023

Software Engineer Intern

LabCorp

  • ›Developed a Microsoft Teams chatbot using Azure APIs and Python to deliver Jenkins build notifications, improving issue tracking.
  • ›Built a React and Node.js MVP allowing users to securely view DNA test results through interactive visualizations.
  • ›Enhanced PostgreSQL (Drizzle ORM) query performance for faster result retrieval and improved UX by reducing load times by 8%.
  • ›Engineered a Python data pipeline to normalize raw DNA test data, accelerating downstream analysis and improving classification accuracy.
React
Node.js
Python
Azure
PostgreSQL
Drizzle
May 2020 – Aug 2020

Software Engineer Intern

Citrix

  • ›Built a Jenkins plugin to automate testing/staging of dependency upgrades, reducing manual maintenance overhead.
  • ›Contributed to an Azure-hosted evaluation platform that assigns efficiency scores based on scalability and execution time.
  • ›Integrated Azure Kubernetes Service into a serverless deployment pipeline, cutting cloud resource usage by 20%.
Azure
Kubernetes
Jenkins
CI/CD
Aug 2019 – May 2020

Software Engineer

Willis Towers Watson

  • ›Developed a .NET API to encrypt and store payment information, enabling secure and seamless user transactions.
  • ›Refactored session management to optimize login performance, achieving a 500ms reduction in page load time.
  • ›Implemented automated testing with Selenium, cutting manual QA effort by 50%.
.NET
Selenium

Projects

Most of my code is on github

GoodDeal

Game Deals notification app

A real-time notification system built with Node.js and React that tracks and alerts users about video game price drops and deals across multiple digital stores. Integrates with gaming marketplaces APIs to provide timely updates and personalized alerts.

Project Link

MW-M31-VTK

Visualization of a Andromeda Galaxy

A scientific visualization project using VTK and Python to create an interactive 3D model of the Andromeda Galaxy (M31). Processes astronomical data to render detailed views of the galaxy's structure and enables exploration of its features through an intuitive interface.

Project Link

WiseOwl

Image recogntion desktop app

A desktop application comparing the performance of pre-trained and retrained machine learning models in classifying owl and eagle images. Demonstrates the impact of model fine-tuning on classification accuracy and processing speed.

Project Link

galactica.ts

const quote = {
  text: "All of this has happened before. All of this will happen again.",
  subtext: "So say we all.",
};