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AI Résumé Analyzer
AI Résumé Analyzer is a generative AI web application that helps
candidates evaluate how well their résumé aligns with a target job
description. Users upload a résumé, paste a job posting, and receive a
structured analysis of matched skills, missing qualifications, and
explanatory context.
Live Demoresume-analyzer.jeffrey-ross.me
ArchitectureGateway-routed multi-service AI platform
Typical Flow~5–15 seconds depending on file size and model latency
System Highlights
LLM-powered semantic matchingPDF and DOCX parsingExplainable output formattingGateway-based routingContainer-ready deployment
Overview
The application addresses a common problem in the job search process:
candidates often know they are broadly qualified for a role, but they
lack fast, actionable feedback on how their experience maps to a
specific job description. Rather than relying on simple keyword
matching, the system uses large language models to compare résumé
content and job requirements semantically, then returns structured
findings that are easier to interpret and act on.
Résumé Analyzer is also part of a broader project,
jr-portfolio-projects, a multi-service platform
designed to host independent AI-driven demos behind a unified gateway.
That architecture lets each application evolve independently while
still being served through a single public entry point.
Problem
Candidates frequently tailor résumés manually, which makes it
difficult to quickly understand where they align strongly with a role
and where they may be underselling relevant experience. Existing tools
often reduce the problem to keyword frequency, which can miss context,
transferable skills, and reasoning behind a match decision.
On the implementation side, the application needed to support multiple
document formats, file upload flows, AI prompt orchestration, and
deployment into a larger multi-service environment without becoming
tightly coupled to the rest of the platform.
Solution
The solution combines document parsing, prompt construction, and
LLM-based reasoning in a simple workflow. Users upload a PDF or DOCX
résumé, provide a target job description, and receive analysis that
focuses on:
matched qualifications and skills
missing or weakly represented requirements
explanatory context behind the assessment
clearer visibility into how a résumé might be improved for a target
role
Diagram: End-to-End Request Flow
The application flow combines file parsing, structured prompt
construction, and LLM reasoning to produce explainable match output.
Technical Architecture
Frontend
HTML, CSS, JavaScript
Responsive upload and results UI
Progress feedback and structured result display
Backend
Python with Flask
PDF parsing with PyPDF2
DOCX parsing with python-docx
Prompt preparation and response formatting
AI Layer
OpenAI model integration
Semantic comparison of résumé vs. role requirements
Human-readable explanation of strengths and gaps
Deployment Pattern
Standalone local execution for rapid testing
Gateway-routed multi-service architecture
Container-ready design for AWS Lightsail deployment
Diagram: Platform Architecture in jr-portfolio-projects
Résumé Analyzer is one of several small applications designed to run
behind a unified gateway, making the overall platform easier to
extend and deploy.
Results & Impact
Delivered a working AI-powered application for résumé-to-role
evaluation
Created a reusable project pattern for additional AI-driven demos
Validated standalone execution and gateway-based routing locally
Established a clear path toward Docker and AWS Lightsail deployment
Key Takeaways
Useful AI applications depend on solid integration between file
ingestion, prompt design, output formatting, and user experience.
Multi-service architecture makes it easier to add new demos without
tightly coupling them into one codebase runtime.
Gateway-based routing provides a clean operational pattern for
hosting several independent AI applications behind a single public
entry point.