AI Resume Screening System Needed

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@corpmember29

Hello!! "AI Resume Screening System "I have similar kind of expertise and work experience. I am having more then 11+ years of experienced in programming and i believe that i can start working step by step and achieve the project goal in short time frame. >>> 40-45 hours weekly I am available for work<<<< >>> you will track all progress of the project thru the tracker <<< I WILL PROVIDE 2 YEARS OF FREE ONGOING SUPPORT AND COMPLETE SOURCE CODE. WE WILL WORK WITH AGILE METHODOLOGY AND WILL ASSIST YOU FROM ZERO TO PUBLISHING ON STORES. successfully implement this project from start-to-finish. Let's come together and create a platform that not only propels your business but also stands out prominently within the marketplace. Awaiting for your positive response. Thanks Christina >>>>>>> We'll share our portfolio in Chat. Let's talk further speak over the freelancer call or chat. <<<<<<

@ShaikhAneesa

Hi there, I understand you need a fast, reproducible AI resume-screening pipeline that ranks entry and mid-level candidates primarily by education and work experience while clearly identifying mismatches. I’m confident I can build this as a transparent, configurable system that your team can adjust as hiring criteria evolve. My approach is to use Python, PyMuPDF/python-docx, spaCy, Hugging Face and embeddings to extract structured education, graduation dates, institutions, job titles, industries and employment history from PDF, DOCX and TXT resumes. I’ll build a weighted scoring engine that prioritises degree relevance, graduation date, institution, years of experience and title/industry alignment, producing ranked candidates with confidence scores and explainable scoring factors. I’ll provide CSV/JSON output with an ATS-friendly structure, with a lightweight dashboard if required. The pipeline will be optimised for batch processing and designed to handle 500+ resumes within the one-hour target on standard cloud hardware. I’ll validate the ranking against your recruiter benchmark, specifically targeting 85%+ precision for the top 10% shortlist, with error analysis to identify where the model needs adjustment. Will you provide the 500+ sample resumes and recruiter-labelled benchmark set, including the education and experience criteria used for the manual ranking? I’m ready to start immediately. Warm Regards, Aneesa.

@Rekhathakur

Hi, I can build this as a lightweight, reproducible resume-screening pipeline focused primarily on education and relevant work experience. My proposed workflow: 1. Resume ingestion: Process PDF, DOCX and TXT files and extract structured fields such as education, degree, institution, graduation date, job titles, companies, industries and employment duration. 2. NLP matching: Use Python with spaCy/Hugging Face and embeddings where useful to identify relevant degrees, titles and experience. 3. Scoring engine: Create configurable weights for degree relevance, graduation date, institution, years of experience and title/industry alignment. 4. Ranking: Generate an ordered shortlist with transparent scores and confidence indicators, plus clear reasons for the ranking. 5. Output: Provide JSON/CSV output and a lightweight dashboard that can later connect to your ATS. 6. Evaluation: Run the system against your recruiter benchmark set, measure precision/recall and iteratively tune the scoring model toward the 85% precision target. 7. Performance: Optimize batch processing so 500+ resumes can be processed within the one-hour requirement on standard cloud infrastructure. A few questions: * What ATS are you planning to integrate with? * Can you provide the recruiter-labeled benchmark dataset? * Approximately how many different job descriptions will the system initially support? * Should resumes be evaluated against each specific job description or one universal scoring model?

@tangramua

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@InterconnectBPO

I can help streamline your hiring process with an AI-powered resume screening system that efficiently identifies the best candidates for your entry-level and mid-level roles. My approach will ensure that the workflow is lightweight and reproducible, leveraging the latest NLP libraries. I understand the need for a model that prioritizes education and work experience while providing a simple output format for your ATS. Processing 500 resumes in under an hour is crucial, and achieving a shortlist of the top 10% with high precision will be a priority. With expertise in Python and experience deploying similar ML models, I can create a scoring logic that meets your specific requirements. This will include clear instructions for your team to adjust as needed.

@topcreator100

Hello, Roman, your project requires a professional like me with a strong skill set in Python programming, emphasis on NLP and experience building robust AI solutions. I have successfully designed and deployed similar machine learning models for resume screening that have garnered automated accolades, increased productivity, and consistently delivered meaningful results to the hiring process. Being fluent with powerful NLP libraries including spaCy, Hugging Face, and OpenAI embeddings, I'm confident to create an efficient workflow that parses documents, PDFs, DOCXs or text files, extracting key information such as educational background and work experience. With this structured data in place, the model will then compute an inclusive score weighing factors like degree relevance, graduation date, institution, total years in role and direct title/industry alignment. Drawing on my background in database design and performance tuning with scalable deployments to cloud hardware, I guarantee a lightweight pipeline implementation that will process over 500 resumes within an hour without sacrificing quality. Additionally, I commit to achieving at least 85% precision using the benchmark sets you'll provide. Your user-friendly JSON/CSV output can be conveniently plugged right into your ATS for a seamless integration experience. All this power packed into your requirements will ensure we surpass your expectations while giving your team autonomy over retraining Thanks!

@maxscender

I’ll build you a resume screening pipeline that turns unstructured applicant files into a ranked shortlist, engineered specifically to solve the bottleneck of manual review. The system will ingest PDFs, DOCXs, and text, parse them into structured data, and apply a scoring model that weighs degree relevance, graduation date, institution prestige, role tenure, and title/industry alignment to output a JSON or CSV file compatible with your ATS. The core design will prioritize precision on your benchmark set, ensuring the top 10% of candidates are surfaced with confidence scores, and the entire codebase will be modular so your team can easily adjust the weighting logic as hiring needs evolve. I’ll ensure the architecture is lightweight and optimized to process your volume of 500 resumes well under the hour limit, delivering a reproducible, documented solution that meets your 85% precision target.

@denisSolution

Hi, I'm Denis, a developer who has built automated document processing pipelines before. I understand you want a lightweight AI system that scores resumes based on education and experience, returning a ranked shortlist while keeping costs and maintenance low. I’d approach this with a clear pipeline: first extract text from resumes reliably, then normalize and structure the data into fields like education, experience, and job titles. For scoring, I’d use a rule-based system with configurable weights for degree relevance, graduation date, institution reputation, and years of experience, falling back to embeddings if direct matches aren’t clear. The output would be a simple CSV or JSON feed your ATS can consume, and I’ll include clear documentation so your team can adjust weights as hiring needs change. Processing 500 resumes in under an hour on standard cloud hardware is realistic with efficient text extraction and a lightweight scoring model. The bigger challenge will be getting the weighting right to hit 85% precision against your benchmark set. I’d start by testing the scoring logic on a sample of your resumes to fine-tune the parameters before scaling up. One risk is that resume formats vary widely, which can trip up text extraction. I’d handle this by standardizing formats early in the pipeline and adding fallback parsing for common variations. I can start working right away. Let's connect and discuss the details. Thanks, Denis.

@TeamGTW

Hi, I have carefully reviewed your requirements and understand that you need a resume screening system to upload and read resumes, match candidates with job requirements, rank applicants, and make the hiring process easier. I have 7+ years of experience in web development and have worked on similar systems with AI, resume processing, data handling, and web applications. I can build an easy to use system that helps quickly find and shortlist the right candidates. If you can provide more details about the project, I can suggest the best approach and get started. Best Regards, Nikita Gupta

@emilesd617

Hi, I am a full stack AI developer with 8 years of rich experience in software development, with a background in Python, machine learning, natural language processing, data processing, AI workflow automation, and scalable backend systems. I am familiar with Python, NLP, machine learning, AI model development, resume parsing, data processing, Hugging Face, spaCy, embeddings, and API based workflows. For this AI resume screening system, I can build a lightweight Python pipeline that extracts structured education and experience data from PDF, DOCX, and text resumes, then applies configurable scoring for degree relevance, graduation date, institution, years of experience, and title or industry alignment. The results can be returned through JSON or CSV with confidence scores, while keeping the scoring weights easy for your team to adjust and validating the model against your recruiter benchmark. I'm an individual freelancer and can work on any time zone you want. Please contact me with the best time for you to have a quick chat. Looking forward to discussing more details. Thanks. Emile.

@Rapha

Hi, I'm Rafael. I'll design the automation pipeline to ingest PDFs, DOCXs or plain text into normalized records and run a weighted scorer focused on degree relevance, graduation date/institution and total years/title alignment. I’ll aim for processing 500+ resumes per hour on standard cloud instances so you can meet the acceptance benchmark. Practical approach: combine a rule-based parser and institution-name normalizer with a lightweight embedding-based title matcher, and keep weights in a simple config your team can tweak. Do you want a hosted dashboard or just JSON/CSV outputs? Contact me through Freelancer chat to discuss this project and estimate realistic timing and cost.

@toriquldev123

Hello There! I’m Md Toriqul Islam, an experienced full-stack developer specializing in Python, AI/ML, NLP, document processing, and API-driven automation. I understand you need an AI-powered resume screening pipeline that extracts structured data from PDFs/DOCX/text, scores candidates based on education and experience, ranks the top applicants, and provides confidence scores. I can build the pipeline with scalable processing, recruiter benchmarking, and configurable scoring weights. My skills in Python, spaCy, Hugging Face, OpenAI embeddings, NLP, Pandas, and ML deployment align well with your requirements. I’m ready to build a quick prototype first and can provide clear documentation for retraining and adjusting the scoring logic. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@mayr81

Build an AI-powered resume screening workflow that ingests PDFs/DOCX/text, extracts structured signals, and ranks candidates for entry-level and mid-level roles. The pipeline will parse documents into normalized fields (education, graduation date, institutions, titles, experience length, and industry alignment), then apply weighted scoring to produce an ordered shortlist with confidence scores. Output will be provided as JSON/CSV (and dashboard views if needed) that can plug directly into your ATS. Performance focus: optimized batch processing to handle at least 500 resumes in under an hour on standard cloud hardware, surfacing the top 10% most qualified applicants. Model evaluation will be structured to reach ≥85% precision against the human-recruiter benchmark set you provide. Deliverables include reproducible code, clear configuration for weighting adjustments, and instructions your in-house team can use to retrain or tune the scoring logic as hiring goals evolve. Sincerely Versatile.

@ImranAli01

Hi there, I got that you need a lightweight AI resume-screening workflow that can process 500+ resumes efficiently, rank candidates primarily on education and work experience, and clearly flag strong matches and obvious mismatches. This is what I can help you with, let's chat. My approach is to build the pipeline in Python using reliable PDF/DOCX extraction, NLP with spaCy/Hugging Face or embeddings where appropriate, and a transparent scoring model. I’ll structure the scoring around degree relevance, graduation date, institution, years of experience, job title, and industry alignment, then generate confidence scores and an ordered top-10% shortlist. The system will be designed for batch processing, reproducibility, and easy adjustment of weighting as your hiring criteria evolve. As final deliverables you will receive the resume-processing pipeline, structured candidate data, scoring and ranking engine, mismatch detection, CSV/JSON output or lightweight dashboard, performance validation against your recruiter benchmark, and clear documentation for modifying weights or retraining the model. Would you prefer the first version as a simple CSV/JSON pipeline for ATS integration, or a lightweight dashboard for recruiters to review and validate the rankings? I’d be happy to review your benchmark resumes and sample job requirements and build a quick prototype around your actual screening criteria. Regards, Imran A.

@rosscarter1011

Hi, ranking resumes by education and experience sounds straightforward until different titles, degree names, graduation dates, and career paths make equally qualified candidates look very different to the scoring logic. The important part is establishing the scoring against the recruiter benchmark first, then testing where the system disagrees with human decisions before expanding the weighting model. That should make the 85 percent precision target measurable rather than just assumed. Should the benchmark set already contain the recruiter’s preferred rankings, or only a qualified versus unqualified decision?

@temoberishvili

Hello! This is Temo, and I see you're looking for an AI-powered resume screening system. I understand the goal is to automate the review of incoming resumes for entry-level and mid-level positions, which can save you significant time and effort. With around 10 years of experience in AI model development and workflow automation, I can definitely help you build a solution that meets your needs. To get this project rolling, I suggest we start by defining the key criteria for screening resumes. From there, we can develop a machine learning model tailored to your specifications. I’ve worked on similar projects like an automated candidate tracking system and an AI-based document analysis tool. Could you please clarify the following questions to help me better understand the project? Q1: What specific attributes or keywords do you want the AI to focus on when screening resumes? Q2: Are there any particular programming languages or frameworks you prefer for this project? Q3: How do you envision integrating this system into your existing recruitment process? Let's create something effective together!

@Aaleen110

The real work here is getting those documents into a usable format, so I would start by building out a PDF and DOCX parser using Python, likely with `PyMuPDF` for PDFs and `python-docx` for Word files, feeding that into a `spaCy` pipeline to get tokens and then sentence structures, which then lets me extract named entities like institutions and dates. The scoring logic will weigh those extracted fields, prioritizing degree relevance and years of experience, so I will set up a weighted system in Python to calculate those scores and rank applicants, returning a JSON output with their ordered shortlist and confidence scores. Preferred Freelancer here, and I have not missed a deadline or gone over an agreed price yet. I need to know if you have a specific list of degrees you want considered "relevant" or if I should use a broader definition. I would have the data ingestion and initial parsing working on day one.

@ozbas

As a seasoned Full-Stack software engineer with over a decade of experience, I understand the importance of efficient, accurate and scalable solutions like the one you are looking for. My substantial multi-solution development and profound knowledge in various technologies will enable me to create an AI-powered workflow that matches your requirements seamlessly. To deliver measurable success, I'm committed to building not just an efficient document parsing system but also a reliable classifier. I emphasize well-thought-out scoring logic based on factors such as degree relevance, institution reputation and total years in role which will be instrumental in the critical task of ordering shortlists. Furthermore, I'm adept at building clear yet comprehensive instructions, empowering your team to tweak things when necessary – a valuable skillset you specifically sought. Beyond just technical competence though, my promise is dedicated after-sales support and effective communication. Let's discuss further how we can adapt my capabilities for your specific project! I am eager to serve you well and help your business succeed by targeting truly qualified candidates from up to 500 resumes within an hour on standard cloud hardware while maintaining.