CV Parser and Matcher
Budget: $750 – $1,500 USD
Looking for an ML Engineer/Full stack developer with already developed web ready CV/resume parser that includes:
• job parser,
• job matching,
• search engine with boolean search capabilities.
1. Parser:
A CV/resume parser helps us automatically store, organize, and analyze resume data to find the best candidate. It is a component that automatically segregates the information into various fields and parameters like contact information, educational qualification, work experience, skills, achievements, professional certifications, publications to quickly help you identify the most relevant resumes based on our criteria.
• It should parse resumes of all formats including PDF, doc, docx, HTML, RTF
• It should be easy to integrate with our existing website.
• It should contain a detailed library of taxonomies to identify candidate skills. (To use lightcast and https://huggingface.co/facebook/bart-large-mnli)
• It should parse multilingual resumes/CVs that automatically identifies region and language to parse information. (English, German, French, Spanish, Chinese, Portuguese, Russian)
• It should extract the complete resume information in maximum data fields.
• Uses deep learning algorithm for improved extraction and smarter identification of resume data for better search results.
• Include bulk import that allows a resume/job parser to parse multiple resumes/jobs in a go.
2. Match Engine:
Search & Match Engine automatically provides similar matches for Resume/CV and JD/Vacancy in the following ways:
• Resume/CV to JDs/Vacancies
• JD/Vacancy to Resumes/CVs
• Resume/CV to Resumes/CVs
• JD/Vacancy to JDs/Vacancies
• Customizable category weights for the ranked resumes (ability to change weighting on things like experience, skills, management, industry, education, etc)
• Real-time ranking updates as weights are changed on web UI
• Cache mechanism to reduce re-computation of parsing & matching if nothing has changed
3. Search Engine
Skills auto-complete on web UI search from integrated search engine
• job parser,
• job matching,
• search engine with boolean search capabilities.
1. Parser:
A CV/resume parser helps us automatically store, organize, and analyze resume data to find the best candidate. It is a component that automatically segregates the information into various fields and parameters like contact information, educational qualification, work experience, skills, achievements, professional certifications, publications to quickly help you identify the most relevant resumes based on our criteria.
• It should parse resumes of all formats including PDF, doc, docx, HTML, RTF
• It should be easy to integrate with our existing website.
• It should contain a detailed library of taxonomies to identify candidate skills. (To use lightcast and https://huggingface.co/facebook/bart-large-mnli)
• It should parse multilingual resumes/CVs that automatically identifies region and language to parse information. (English, German, French, Spanish, Chinese, Portuguese, Russian)
• It should extract the complete resume information in maximum data fields.
• Uses deep learning algorithm for improved extraction and smarter identification of resume data for better search results.
• Include bulk import that allows a resume/job parser to parse multiple resumes/jobs in a go.
2. Match Engine:
Search & Match Engine automatically provides similar matches for Resume/CV and JD/Vacancy in the following ways:
• Resume/CV to JDs/Vacancies
• JD/Vacancy to Resumes/CVs
• Resume/CV to Resumes/CVs
• JD/Vacancy to JDs/Vacancies
• Customizable category weights for the ranked resumes (ability to change weighting on things like experience, skills, management, industry, education, etc)
• Real-time ranking updates as weights are changed on web UI
• Cache mechanism to reduce re-computation of parsing & matching if nothing has changed
3. Search Engine
Skills auto-complete on web UI search from integrated search engine