The potential answer should be based on the historical data of accepted candidates.
One can frame this as ranking or recommendation problem which are common approaches. like Education, categorical, experience, numeric, resume keywords-TF-IDF/embeddings.
Feature Engineering: Encode education as categories, Use experience as a numeris value and also Turn resume keywords into numbers using TF-IDF or proberbly embeddings.
Model Training. Train a supervised model like neural network or XGBoost using your historical accepted date against non accepted data
Ranking. Rank candidate by their their predicted probability score to get your top 10
Scalability. Impute the use of simplarity search to quickly compare candidates.