An NLP job recommender launches on enbek.kz

Congratulations to our community member Issagali Konysbayev, who developed a unique resume-based job recommendation system using advanced NLP models.

Working with IT specialists and analysts from the Workforce Development Center, the system was adapted and deployed on enbek.kz.

The service is built with FastAPI, sentence_transformers, and PyTorch. Its core is an NLP model that converts resume and vacancy text into embedding vectors. After jobs are filtered by parameters such as region or profession, these vectors are used to calculate cosine distance between vacancy vectors and the resume query vector. The system selects the nearest vectors by cosine similarity.

A distinctive feature is that the system learns not only from current occupations, but also from the semantics of an applicant's education, skills, and previous work experience. Issagali used a checkpoint from the middle of training rather than the final model to avoid overfitting solely to current occupations.

Recommendations are fast because the system uses only a retriever model, without an additional reranker classification model. The model was trained with ContrastiveLoss. Although Issagali did not have time to test other loss functions, there remains substantial potential for further improvement.

According to a Workforce Development Center press release, the project both demonstrates important innovation in recruitment and opens new possibilities for applying NLP to real labor-market problems.