LLM-based Job Search, Match, and Apply
About the Project
The objective of this project is to develop a pipeline that will extract job postings from websites, analyze the required skills and responsibilities of the position, and produce recommendations for my personal and professional development that align with my skills and interests. This will ensure that I remain competitive in the job market, and it will also benefit those who are seeking data scientist jobs.
Process
The core of this system is the synergistic combination of a MCP and a LLM designed to revolutionize the job application and resume customization process.
The MCP serves as the initial layer, systematically crawling and extracting a vast volume of raw job postings from diverse internet sources. This capability ensures a broad and up-to-date dataset for subsequent analysis, overcoming the limitation of manually searching and collating relevant opportunities.
Upon ingestion by the LLM, the extracted job postings undergo a sophisticated categorization and decomposition process. The LLM's natural language understanding capabilities allow it to dissect the job description into a highly structured, three-column output. This structure includes a clear articulation of the core responsibility and expected outcomes of the role, a precise listing of the necessary requirement skills, qualifications, and experience, and a critical, generative column called estimated activities. This last column is where the LLM extrapolates the probable, specific, day-to-day tasks and projects a successful candidate would undertake to fulfill the listed responsibilities and requirements, moving beyond the general language of the posting to infer the operational reality of the job.
The system's most powerful analytical feature is the comparative analysis phase, or gap identification through activity mapping. The LLM takes the structured estimated activities derived from the job posting and compares them directly against the documented professional activities detailed in the applicant's resume. This comparison is not a simple keyword match but a semantic evaluation designed to Identify Missing Links by pinpointing specific activities or experience detailed in the job posting's estimated activities that are either completely absent from the current resume or not described with sufficient detail or relevance. This process is highly effective because most individuals do not include the exhaustive list of all their professional activities on a generalized resume. Furthermore, by comparing the applicant's recorded achievements against the estimated needs of the role, the analysis provides a quantitative and qualitative evaluation, known as quantify resume alignment, of the current resume's suitability for the specific job description and its underlying requirements.
This detailed analysis acts as a blueprint for strategic resume customization and optimization. The LLM not only highlights the gaps but also enables the user to strategically customize their resume. This allows the applicant to Incorporate relevant experience by selectively pulling in previously unlisted but relevant activities from their professional history to address the identified missing links, thereby directly bolstering their candidacy for the specific role. Additionally, the LLM’s deep understanding of the job posting's nuances allows for the refinement of the resume's language to tailor language and focus, ensuring that the applicant’s experience is presented in a context and terminology that precisely tackles the advertised job description and the employer's specific needs.
This entire process transforms the generalized job application into a highly strategic and customized submission, significantly increasing the probability of advancing past initial screening stages.