This thesis explores the use of various Natural Language Processing (NLP) methods of automated matching between job seekers and potential employers. In the study, two different text representation techniques are evaluated, sentence embeddings and character n-gram. Keyword extraction using KeyBERT is investigated as a potential enhancement to this matching process. Additionally, cosine similarity and L1 distance are compared to understand which similarity metric is best for the task. Using a dataset of 482 CVs and 63 companies, the research investigates the effectiveness of these methods in capturing the nuances of job titles and applicants' profiles. The results indicate that sentence embeddings on the full text of a CV using cosine similarity perform the best for this task in terms of accuracy. The findings highlight the challenges of aligning the CV content with specific job titles sought by companies. The study's contribution includes identifying the optimal feature extraction method for automated applicant-company matching and highlighting the limitations of keyword extraction in this context.