AI Job Matching System – Annotator/Data Labeling Contributor
Participated in the design and implementation of an AI-driven job and attachment matching system, focusing on training, validating, and testing AI models using resume and job description text data. Key tasks included cleaning, preprocessing, annotating, and labeling large datasets for semantic similarity, skill extraction, and candidate-job fit classification. The project emphasized fairness-aware algorithms, explainable AI, and generation of ground truth labels for evaluation and benchmarking. • Labeled and prepared hundreds of resume–job description pairs for machine learning training, validation, and accuracy testing. • Utilized techniques like TF-IDF, BERT embeddings, and manual annotation for feature engineering and skill labeling. • Evaluated model predictions and created labeled ground truth for classification and ranking performance comparison. • Assisted with dataset cleaning, error detection, and integrity verification steps during the labeling workflow.