Job recommender systems (JRS) are the most flexible and efficient tools that can not only provide relevant job opportunities to jobseekers but can simplify the task of employers by providing options like skill mapping and discovering more relevant job candidates. Modern JRS can be efficient in providing features like relevance, reliability, availability, and lower operational cost. However, there is a dire need to investigate the current issues and challenges in the available approaches used for job recommender systems. To achieve this goal, a systematic review-based study was conducted, and its protocol and findings are reported in this paper. The aim of this study was to chart out the state-of-the-art approaches in the domain of job recommender systems and uncover key issues and challenges in the current approaches. For this purpose, a protocol was followed to search and identify existing contributions in the domain between 2015 and 2024 that were found close and relevant to the theme of this study and consequently, seventy-three (73) studies were screened and reviewed. The results of this SLR manifest that 52.94% of the studies are presented in the generic content-based filtering. In knowledge-based recommender systems, generic and profile-based techniques have share of 27.27% each. In hybrid job recommender systems, the commonly used technique is generic that has share of 50%. In collaborative-based job recommender systems, the commonly used technique is matrix-factorization that has a share of 30%. In data mining-based recommender systems, machine learning has a share of 37.93% whereas deep learning has a share of 17.24%. The finding of this systematic literature review-based study will aid the researchers to design the job recommender systems that are more effective and efficient. Regarding future contributions and collaborations, cold start, skill mapping and transparency are the quality attributes that can be addressed; JRS is one of the emerging tools due to its prevalent acceptance and success that opens new ways of recruitment and job hiring.