AutoHire Coach: An Agent-Orchestrated Retrieval-Augmented Framework for Context-Aware Career Preparation

Modern job preparation platforms often rely on static resume parsing or generic large language model prompting, which limits contextual grounding and long- term personalization. This paper presents AutoHire Coach, an agent-orchestrated retrieval-augmented framework designed to perform semantic skill gap analysis, generate personalized project roadmaps, and produce context-aware interview ques- tions aligned with specific job descriptions. The system integrates a FastAPI backend, a React-based frontend, and an embedded Qdrant vector database to enable cosine similarity-based retrieval over dynamically growing knowledge collec- tions. A Llama 3.3 70B model accessed via cloud API performs skill extraction and reasoning, while BAAI/bge-small-en-v1.5 embeddings (384-dimensional) sup- port efficient semantic search. Unlike single-pass prompting systems, the proposed architecture combines retrieval, web search augmentation, GitHub code parsing, and cross-session knowledge persistence to enhance contextual consistency. Ex- perimental evaluation across multiple job descriptions and candidate profiles indi- cates improved relevance and stability of generated outputs when compared to a non-retrieval baseline, while maintaining acceptable response latency. The findings demonstrate that retrieval-grounded agentic orchestration can significantly improve personalization and adaptability in AI-assisted career preparation systems.