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An effective applicant selection procedure for job roles is one of the most significant requirements for organisations human resources (HR) departments. Due to the high number of applicants it is necessary to short-list and rank submitted CVs based on their suitability for the job requirements. To reduce costs, error and time there is a strong desire from companies towards automating the two processes of: specifying the requirements criteria for a given job (experience, skills, etc) and matching between the applicants' profiles and the job requirements; to produce an applicants' ranking policy that gives consistent and fair results which can be legally justified. However both these processes involve a high level of uncertainty, as they require the input of different occupation domain experts in the decision making process. These experts will have different opinions, expectations and interpretations for the requirements specification as well as for the applicants matching and ranking criteria. Determining the consistency and reliability of each expert's decision making behaviours is also necessary to ensure that experts decisions are unbiased and correctly weighted according to their level knowledge and experience. This paper presents a novel approach for ranking job applicants by employing fuzzy agents for handling the uncertainties and inconsistencies in group decisions of a panel of experts. The presented system will enable automating the processes of requirements specification and applicant's matching/ranking. Experiments have been performed within the residential care sector in which the proposed system has been shown to produce ranking decisions that were relatively highly consistent with those of the human experts.