Gemini AI Resume Screener

@Microlent

Hello, I am Dr. Rajesh Rolen, PhD in Computer Science & Engineering, with experience of over 20+ years in PHP, Web Development, Node.js, Software Architecture, AI Chatbot Development, Python, Django As a preferred freelancer in the top 1%, I have done 400+ projects here on freelancer.com, I have 4.9 ratings out of 5 on average, which showcases my quality of work and timely delivery. Key Highlights: - Free Hosting Support on the Cloud or any desired platform. - Free 3 months of post-delivery support to ensure that our client doesn’t face any challenges after the launch of the project. - Free Dedicated tester on projects to ensure quality delivery, so clients don’t need to act as a tester. - 10+ Years experience UI/UX team to ensure intuitive UI. Portfolio: https://www.freelancer.com/u/Microlent Please open the chat and send me a message, so we can have a more detailed discussion about the project to give you the project timeline and cost. Thank you for considering my services. I look forward to engaging in a productive conversation and understanding how I can be of assistance in bringing your project to life. Regards Rajesh Rolen

@crtvecode

I see you're looking to build a resume-screening module using Gemini AI. The key here is to make sure that the application can efficiently parse and analyze multiple resumes, while providing accurate scoring based on your defined criteria. I’ll focus on optimizing the upload process to handle at least 50 resumes in under 2 minutes, and making sure keyword matching and experience scoring are precise within the ±5% range. And, I can complete this module first and then let you test it out. You’ll only need to create a milestone once you’re happy with the results, so this way, there’s no risk for you. I’m ready to start immediately and can deliver a source-controlled codebase, a deployed demo, a video walk-through, just to make sure everything is clear, and a user guide as specified for 24999 INR. Looking forward to getting this live for you. Those tools I mentioned will really help simplify the process. Rajavel - Your go-to for React, Node, and AI solutions.

@axiomswb

Your Gemini AI Resume Screener — ranked candidates from a 50-resume batch in under 2 minutes, no manual filtering. I have built exactly this stack before and here is my concrete execution plan: Document Ingestion: A bulk-upload handler in React plus a Python API layer accepts PDF, DOCX, and LinkedIn exports and queues them for parallel Gemini processing. Parallel calls rather than sequential ones are what hit your 2-minute acceptance criterion reliably. Gemini Scoring Pipeline: Structured prompts extract keywords, calculate years-per-skill, and return a normalized fit score against the role template you define. I will add a consistency check pass where Gemini cross-validates its own experience claims against raw resume text, which catches the hallucinated tenure figures that quietly break scoring accuracy past your plus-or-minus 5 percent threshold. Ranked Results Dashboard: Drill-down UI with AI rationale per candidate, CSV export, and REST endpoints ready for ATS push, backed by PostgreSQL caching role templates and scores. Cloud Deployment: AWS free tier, clear README, video walkthrough, and PDF user guide as specified. One problem you have not mentioned: LinkedIn HTML exports vary wildly in structure. I will normalize them through a pre-processing step before they hit the Gemini layer so scoring stays consistent across all three file types. Delivery: 22,500 INR in 5 days.

@gauravmittal1989

I can build your Gemini AI-powered Resume Screener as a web application integrating Google's Gemini API to automate candidate evaluation. With 13+ years of PHP and API integration experience, I'll develop a system where recruiters upload resumes (PDF/DOCX), define job criteria, and Gemini AI scores and ranks candidates with detailed reasoning. Key features: resume parsing, Gemini API calls for contextual screening, a clean dashboard showing ranked candidates with match scores, and export functionality. I'll use PHP backend with a responsive frontend, ensuring secure file handling and API key management. The job description appears truncated, so I'd like to confirm full requirements — but based on what's described, I can deliver a fully functional MVP within 12 days. Happy to discuss further details before we begin.

@bcosley

Hi, I’m helping you cut screening time by turning resumes into structured, scored data. I’ve built similar lightweight screening tools that feed ATS pipelines and keep recruiters focused on final decisions. Execution approach: I'd start by validating the upload flow with a small batch, ensure parsing handles PDFs, DOCX, and LinkedIn exports, then wire the scoring against the role profile and cache results for re-runs. I’d keep the UI lean and the API simple so hosting on AWS/GCP stays straightforward. One technical risk: parsing variability across documents may drift scores; we’ll normalize and test on a representative test set. Two clarification questions: What should happen if a resume fails to parse, should we retry and how should we surface that in the UI? Who owns and maintains role templates, and how will updates be propagated to scoring rules? If we're aligned, I'd be happy to walk you through the implementation plan before we get started. Best regards, Brandon

@AZTURK1

Hi there, Screening 50+ PDF/DOCX resumes simultaneously without hitting Gemini API rate limits or freezing your interface requires asynchronous worker handling. I will set up the screening system using a Python FastAPI backend with parallel task processing, PostgreSQL for template caching, and a responsive frontend with ATS export. What I'll do: ✅ Build the document parsing and scoring pipeline connecting directly to Gemini for experience mapping and fit scoring. ✅ Create the role profile management interface, candidate ranking view, and REST/CSV export features. ✅ Benchmark against your test dataset using simulated batch runs before deployment to ensure scoring matches within ±5% and finishes under 2 minutes. Skills: ✅ Python & FastAPI backend development ✅ React & PostgreSQL database design ✅ Gemini API integration & structured JSON extraction ✅ Docker containerization & AWS/GCP deployment ✅ Microsoft® Certified: MCSA | MCSE | MCT ✅ 300+ projects delivered, 280+ five-star reviews Why me: Fast responses and unlimited revisions until scoring accuracy matches your benchmark. Which Gemini model tier do you plan to use (Flash or Pro), and do you already have a Google AI Studio or Vertex AI key ready with sufficient RPM limits for parallel processing? I can deliver in 1 day for 21200 INR and can start right now.

@Eliteinno

Hey, For resume screening at 50+ documents per batch, the important design choice is separating document parsing, structured candidate extraction, scoring, and Gemini reasoning instead of sending raw resumes through one large prompt. That makes the scoring reproducible, keeps role-specific criteria consistent, and gives you a better path to validating the ±5% acceptance target. I propose a lean React/Node.js or Python application with PDF/DOCX/LinkedIn ingestion, batch processing, Postgres result caching, configurable role templates, Gemini-based structured extraction and rationale, ranked results, CSV export, and REST-based ATS integration. I’ll include processing status and failure handling for large uploads, source-controlled code, deployment, README, user guide, walkthrough, cross-browser checks, and the requested post-delivery refinement. I’ll also design the scoring pipeline so the supplied test set can be used to calibrate keyword, experience, and skill weighting before handover. Do you already have the test resumes and expected scoring dataset available for calibration?

@mayankinnovative

Hi there I can develop a Gemini-powered AI resume screening system that automatically analyzes CVs, extracts candidate information, compares applicants against job requirements, and produces structured screening results. I have 20 years of software development experience and can build a reliable, scalable solution around your recruitment workflow. My approach Resume Parsing — Extract candidate details, education, experience, skills, certifications, and other relevant information from PDF/DOC/DOCX resumes. Gemini AI Integration — Use Gemini to analyze resumes against defined job descriptions and evaluation criteria. Candidate Scoring — Generate consistent, explainable scores and matching insights based on configurable criteria. Search & Filtering — Allow recruiters to filter candidates by skills, experience, score, location, education, and other fields. Recruiter Dashboard — Provide a clear interface for uploading resumes, reviewing results, comparing candidates, and managing screening status. API & Security — Build secure APIs, authentication, file handling, data validation, and appropriate access controls. Quick questions 1. Do you already have a Gemini API key and preferred Gemini model? 2. Which resume formats need to be supported? 3. How should candidates be scored against a job description? 4. Do you need bulk resume upload and batch screening? 5. Should the screener integrate with an existing ATS/CRM? Thanks

@shayankhan333

Parsing a LinkedIn PDF export is trickier than a normal resume PDF, the layout is a single column with hidden Unicode, so the parser needs a dedicated branch before Gemini ever sees the text. I'll build a React front with a Node API, Gemini for scoring against your role template, and Postgres to cache parsed results and rationales. CSV export and an ATS push endpoint included. 1. Which ATS should the REST push target? 2. Can you share the scored test set upfront so I can tune against it? Let's get this moving. Shayan

@DeyaaAhmed

Hi, I can build your AI-powered resume screening module using Django and Django REST Framework for a secure backend paired with React.js for a lean, responsive frontend. I will integrate the Gemini API to parse PDF/DOCX/LinkedIn exports, extract keywords, measure experience levels, and generate fit scores against custom role profiles, caching results in PostgreSQL for fast retrieval and ATS export via REST endpoints. The system will be optimized to process 50+ resumes in under two minutes while maintaining ±5% accuracy on test sets, deployed on AWS/GCP with clear documentation and a video walkthrough for seamless handover. You will receive the complete source code, deployed demo, user guide, and one round of post-delivery UX/scoring refinements. I have extensive experience integrating LLM APIs into production hiring tools, ensuring reliable parsing logic and scalable architecture that aligns precisely with recruitment workflows. I also offer FREE post-delivery support to monitor initial screening accuracy, troubleshoot any Gemini API rate limits or parsing errors, and assist with minor scoring adjustments during the first month. Let's discuss the project in more details.

@ashinnd84

Hello there, we are a team of developers and we can do this project in no time. Please, send me a message to discuss the work. Thanks Ashish Kumar.

@TechizeBuilder

Dear Client, I read "Gemini AI Resume Screener" carefully and understand what you're looking to achieve with this Mern Stack Development project. My hands-on experience with PHP, Python, Node.js, Web Development, AI Model Development aligns directly with what you need. I've delivered similar Mern Stack Development work end to end — clean, scalable and on time — and I can share closely related samples and start right away. A few quick questions to get us started: 1. Do you have detailed requirements ready, or should we define the scope together? 2. What's your target timeline for the first working version? 3. Are there existing designs, code or systems I should build on? Thanks & Regards, Deepak

@KarthikTechCon

Hi, I’m Karthik, with over 15 years of experience in web applications and AI integrations. I’m excited to help you build the Gemini AI Resume Screener. I propose to develop a streamlined resume screening module using React for the front-end and a Node.js API, ensuring efficient Gemini API integrations. I’ll implement a robust keyword matching system and scoring logic for candidate evaluation. The solution will be optimized for quick uploads, with performance benchmarks ensuring the processing of 50 resumes in under 2 minutes. Deliverables will include: - A source-controlled codebase with a clear README. - A deployed demo on an AWS/GCP free tier or your server. - A brief video walkthrough and PDF user guide. - One round of post-delivery tweaks to enhance UX. I’m committed to meeting the acceptance criteria and ensuring a clean UI across all major browsers. Let’s discuss your timeline and milestones to get this project live soon. Looking forward to collaborating! Best, Karthik

@mahads31

As someone who has honed my skills in AI Automation and has extensive experience in Web Development, creating the Gemini AI Resume Screener aligns perfectly with my capabilities. Leveraging Node.js, Python, and my adeptness at handling API calls (like Gemini AI), I can create a powerful web-based tool that will autonomously analyze uploaded resumes for keyword matching, years of experience per skill, and produce an overall fit score as per your defined role profile. One of my core strengths lies in building systems that are not only efficient but also scalable and reliable for long-term use. This means your web application will not only be able to handle large numbers of resume uploads within a minimal timeframe (as per Acceptance Criteria), but it will also ensure data integrity and maintain high-speed performance throughout. Hosting it on AWS or GCP is second nature to me. Moreover, my proficiency in React.js means we can create a clean, intuitive user interface that will render seamlessly across all major browsers – Chrome, Firefox, Safari, and Edge. My dedication doesn't end with just the development phase - I will provide you with a thoroughly documented codebase, offer you a video walk-through of the application along with a PDF user guide, and even accommodate one round of post-delivery tweaks to ensure your utmost satisfaction. Let's discuss the timeline and milestones so we can build a resume screener that sets new benchmarks for efficiency in hiring!

@TeraByte26

Hi Shreenath, I will build a React front‑end and Python/Node API that lets recruiters upload PDFs/DOCXs or LinkedIn exports, parses them with Gemini AI, tags keywords, computes experience scores, ranks candidates, shows rationale, and exports CSV or pushes via REST. I can deliver a functional demo within three weeks for 30 000 INR. Shall I start with a free prototype? Thanks, Rohit Waiting for your response in chat! Best Regards.

@chiragpipal

⚠️ If you're not happy, you don’t pay. ⚠️ Hi, Thank you for checking my proposal and sharing the detailed project brief. I can build your resume screening application using a React front-end and a Node API with a clean, efficient design. I will deliver: • Robust upload functionality for PDF, DOCX, and LinkedIn exports. • Smart keyword matching and experience gauging. • AI-generated scores and rationale for each candidate. • A results page with ranking, drill-down options, and CSV export. • Integration with ATS via REST endpoints. You will also receive: • Source-controlled codebase with clear README. • Deployed demo on AWS or GCP free tier. • Brief video walk-through and PDF user guide. • One round of post-delivery UX adjustments. I am confident I can execute your vision efficiently. Looking forward to discussing timeline and next steps. Best regards, Chirag Pipal

@albertc1996

Hi there, Your requirements are clear and practical. Here is how I will deliver this within your exact criteria: 1. Speed (50 Resumes under 2 min): Instead of serial API calls, I will extract text locally (via pdf-parse/mammoth) and run an asynchronous parallel queue using Gemini's low-latency Flash model. This easily processes 50 resumes within 60–80 seconds. 2. Accuracy (±5% Benchmark): We'll enforce Gemini Structured JSON Outputs with temperature: 0 mapped against your scoring rubric (exact years of experience, skill weights, and keyword presence) to guarantee deterministic, repeatable scoring. 3. Tech Stack: Frontend: React + Tailwind CSS (tested on Chrome, Safari, Firefox, Edge). Backend: Node.js (Fastify/Express) or Python (FastAPI). Database: PostgreSQL for caching profiles, job templates, and rationale. Export: Instant CSV export + REST endpoint ready for ATS integration. Deliverables: Clean repository + documentation, cloud deployment (free tier), short video walkthrough, and 1 round of scoring calibration. I can deliver this in 6–7 days. Let's discuss your rubric to get started.

@ammarahmed20

As an experienced full-stack developer, AI aficionado, and automation enthusiast, I am confident I can bring your Gemini AI Resume Screener to life. My track record includes the development of AI agents and a wide array of SaaS applications, which closely parallels the objectives of your project. My involvement in projects such as Chatbots handling 24/7 support and sales on autopilot has honed my skills in designing and building systems that automate complex processes - a skillset that perfectly compliments your need for a smart filter capable of accurately screening and scoring resumes. I understand the true pain point of manual operation your recruitment team are facing; repetitive tasks overwhelm teams and impede productivity. Using my technical mastery with React, Node.js, Python and profound knowledge of REST API endpoints, developing a lean yet powerful interface for your resume-screening module that integrates seamlessly with Gemini AI is highly feasible for me. With considerable experience in deploying projects on AWS or GCP, I assure you that hosting, scalability, and security won't be an issue.

@FreeLancers360

As a full stack developer specializing in AI-driven solutions, I am confident in my ability to craft the Gemini AI Resume Screener you need. My extensive experience with Web Application Development, Custom Software Development, and AI Integration is a perfect fit for your project. I have a strong command over various frameworks like Laravel, React.js, Node.js, PHP, and more - just the right skills needed to bring your vision to life. My expertise extends beyond just coding. I prioritize delivering secure, scalable, high-performance applications that are architecturally well-crafted. This would guarantee smooth functionality for your recruiter uploads and the subsequent processing through Gemini AI's API. Furthermore, I am well-versed in using REST API endpoints to seamlessly allow CSV export or direct push to an ATS. In terms of hosting, deployment, and database management on AWS or GCP - no problem! My proficiency in these cloud platforms ensures that your Resume Screener can be easily maintained and scaled as your requirements evolve. I am incredibly excited about this project and believe that my thoughtful approach combined with my depth of skillset will make for an extraordinary product that meets all your acceptance criteria and exceeds your expectations at every turn.