Prachi S.

Key Skills

Software

Don't disclose

Top Subject Matter

AI-assisted UI automation and LLM-enhanced DOM analysis for reliable agentic testing

Conversational AI automation

intent classification (NLU)

Top Data Types

Text

Image

Document

Top Task Types

Function Calling

Routing

Classification

Freelancer Overview

Automation Engineer Intern — AI-powered self-healing UI automation (Playwright + LLM DOM analysis). Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose. Education includes Bachelor of Technology, Pranveer Singh Institute of Technology (2026) and Intermediate, U.P. Kirana Seva Samiti Vidyalaya (2022). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Function Calling and Routing.

Labeling Experience

Automation Engineer Intern — AI-powered self-healing UI automation (Playwright + LLM DOM analysis)

Don't discloseFunction Calling

Built and maintained an AI-assisted UI automation framework that improves robustness through multi-step automated recovery of UI element locators. Added LLM-driven DOM analysis and persistent storage of recovered locator strategies to reduce future maintenance and increase automation reliability. Designed the system to support resilient end-to-end execution rather than manual relabeling of UI artifacts. • Implemented multi-layer locator recovery using ARIA attributes, role-based selectors, text-based selectors, CSS, and XPath fallbacks • Integrated LLM-driven DOM analysis to infer or validate DOM elements during automation runs • Used persistent locator storage to retain successful locator strategies for later sessions • Developed the framework using Playwright (Python) for resilient automation workflows

2026 - Present

AI Intern — Conversational AI + IVR integration with NLU intent classification and routing

Don't discloseRouting

Developed and modernized automation that combines IVR systems with conversational AI via event-driven pipelines. Implemented an NLU module to classify user intents and route them to appropriate handling logic, improving response accuracy and automation quality. Strengthened production readiness by adding centralized error handling and input validation in an Agile workflow. • Designed and implemented a custom NLU module for intent classification • Built intent routing logic to improve automation and response accuracy • Integrated conversational AI with IVR systems using event-driven pipelines for scalability • Added centralized error handling and input validation while collaborating in Agile sprints

2025 - 2025

Education

Work History