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Devashishpandey1103

MCP-Powered-AI-Job-Recommendation-Engine

by Devashishpandey1103
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MCP-Powered AI Job Recommendation Engine

**Model Context Protocol (MCP)**와 통합된 AI 기반 채용 추천 및 이력서 매칭 엔진입니다. 이 시스템은 표준화된 MCP 도구를 제공하여 Claude, Antigravity, 맞춤형 LLM과 같은 AI 에이전트 및 어시스턴트가 채용 추천 조회, 후보자 프로필 파싱, 의미 기반 스킬 적합도 점수 계산, 자동 커리어 매칭을 원활하게 수행할 수 있도록 합니다.

🌟 주요 기능

  • Model Context Protocol (MCP) 서버: 표준화된 도구(recommend_jobs, match_skills, parse_resume, filter_jobs_by_location)를 제공합니다.

  • 의미 기반 스킬 매칭: Transformer 임베딩과 코사인 유사도를 활용하여 후보자의 경험을 채용 공고와 매칭합니다.

  • 후보자 스킬 격차 분석: 부족한 핵심 스킬을 강조하고 개인화된 학습 경로를 제안합니다.

  • 인터랙티브 UI 대시보드: Streamlit으로 구축되어 후보자 프로필 업로드 및 실시간 추천 필터링을 지원합니다.

Related MCP server: LinkedIn MCP

🚀 기술 스택

  • 프로토콜: Model Context Protocol (MCP Python SDK)

  • AI 및 NLP: LangChain, SentenceTransformers, Scikit-Learn, PyTorch

  • API 및 프런트엔드: FastAPI, Streamlit, Pandas, NumPy

📁 저장소 구조

MCP-Powered-AI-Job-Recommendation-Engine/
├── mcp_server/
│   ├── __init__.py
│   ├── server.py              # MCP Server implementation & tool definitions
│   └── tools.py               # Recommendation tool implementations
├── engine/
│   ├── __init__.py
│   ├── resume_parser.py       # Resume skill extraction engine
│   ├── matcher.py             # Semantic similarity & fit score calculator
│   └── job_database.py        # Job listings & metadata store
├── frontend/
│   ├── app.py                 # Streamlit UI dashboard
├── data/                      # Sample resumes & job description datasets
├── notebooks/                 # Experimentation & embedding evaluation
├── tests/                     # Unit test suites for MCP tools & matcher
├── requirements.txt           # Dependency manifest
└── README.md                  # Project documentation

🛠️ 시작하기

1. 저장소 클론하기

git clone https://github.com/Devashishpandey1103/MCP-Powered-AI-Job-Recommendation-Engine.git
cd MCP-Powered-AI-Job-Recommendation-Engine

2. 환경 설정 및 의존성 설치

python -m venv venv
# Windows:
venv\Scripts\activate
# macOS/Linux:
source venv/bin/activate

pip install -r requirements.txt

3. MCP 서버 및 웹 앱 실행

# Start the MCP Server (stdio / SSE transport)
python mcp_server/server.py

# Start the Streamlit Dashboard
streamlit run frontend/app.py

Advanced AI Systems 및 Model Context Protocol 포트폴리오의 일환으로 개발되었습니다.

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