OpportunityScout: An Autonomous Intelligent Agent for Context-Aware Career Opportunity Discovery, Semantic Eligibility Matching, and Multi-Objective Ranking

College students spend hours searching for internships, hackathons, and entry-level jobs across scattered platforms. Popular job boards rely on basic keyword matching and long, flat lists. They do not tell students why an opportunity fits them, whether they meet the actual requirements, or how soon the deadline closes. This paper presents OpportunityScout, an intelligent web agent that automates opportunity discovery, evaluates student eligibility using Large Language Models (LLMs), and ranks listings using a balanced multi-factor formula. Built with a FastAPI backend and a modern React dashboard, OpportunityScout scrapes multiple platforms at the same time—including Internshala, Unstop, Devpost, and top company Applicant Tracking Systems (Greenhouse and Lever)—using polite, safe web crawlers. The system automatically reads PDF resumes, groups opportunities into batches of 10 to cut AI token costs by about 90%, and uses models like Meta Llama-3.3-70B and GLM-4.7-Flash to generate match scores and concise reasons. It then ranks jobs based on a mix of student eligibility (50%), deadline urgency (30%), and stipend or prize amount (20%). It also includes a tailored application assistant that suggests resume points, missing skills, and cover letter opening lines for any selected job. Twelve functional test cases covering scraping, resume parsing, AI batching, ranking, and automated email alerts were tested and all passed. OpportunityScout turns a stressful, manual job hunt into an easy, automated daily routine.