Experience: Min 3+ years of experience working with resume parsing , NLP , or text extraction systems. Experience in building or integrating AI-driven resume parsing tools . Familiarity with ATS systems or similar recruitment-related software is a plus. Programming Languages : Proficiency in Java (or Python if you are open to using Python-based solutions) for implementing parsing logic. Knowledge of Regex for text extraction and pattern matching. Natural Language Processing (NLP) : Experience with NLP libraries like SpaCy , Stanford NLP , or Apache OpenNLP for Named Entity Recognition (NER) and text classification. Familiarity with text preprocessing techniques, such as tokenization, stemming, lemmatization, and stop word removal. Machine Learning : Experience with machine learning models for entity extraction and classification. Familiarity with frameworks like Scikit-learn , TensorFlow , or PyTorch for building and training custom models for resume parsing. Text Extraction : Proficiency in using libraries like Apache Tika , Apache POI ,
or PDFBox for extracting text from various resume formats (PDF, DOCX, etc.). Data Structuring : Experience in transforming raw text into structured data (JSON, XML, or database entries). Knowledge of data normalization and handling inconsistent formats in resumes. Database Management : Experience with database systems like MySQL , PostgreSQL , or MongoDB to store parsed resume data. Knowledge of data modeling for structuring resumes in a database. Additional Skills: Data Cleaning & Preprocessing : Expertise in cleaning, transforming, and normalizing text data to improve parsing accuracy. Testing & Optimization : Ability to test the parsing system on various resume formats and optimize the parsing logic for better accuracy and speed. Familiarity with unit testing and test-driven development (TDD) for parsing functionalities. APIs & Integration : Experience in building or integrating with APIs for resume parsing (if you plan to integ