Enhancing Data Applications: LLM-Powered Resume Writing Streamlit App

Role: Developer & AI Engineer
Status: Beta Testing
Type: AI Application, Interactive Web App

Project Motivation

This project was born directly out of the time-consuming, repetitive, and often frustrating experience of writing tailored resumes for each job application. As someone actively navigating the job market, I found myself spending hours revising resumes to match job descriptions — rephrasing skills, rewriting bullet points, and reorganizing content to align with applicant tracking systems (ATS).

Realizing that large language models (LLMs) like OpenAI’s GPT and Ollama could transform this tedious process, I set out to build an AI-powered resume writing assistant using Python and Streamlit. My goal was to automate the first draft of resume customization, reducing manual effort while improving consistency and alignment to job postings.

Development Journey:

Turning Frustration into Automation

Identified pain points in manual resume writing: repetitive edits, keyword tailoring, and time lost per application.
Designed a Python backend to integrate an LLM capable of ingesting job descriptions and generating tailored resume drafts.
Focused on prompt engineering to ensure outputs were context-aware, role-aligned, and ATS-optimized.

Building the Streamlit Interface

Created an intuitive web app with job description input, model settings (temperature, max tokens), and resume output display.
Added real-time editing features and download options for fast iteration.
Ensured a smooth user experience for non-technical users.

Ran multiple real-world tests using my own job applications to fine-tune the quality and relevance of generated resumes.
Incorporated feedback loops, like highlighting mismatched sections or flagging missing skills, to improve output reliability.