Project Type
Objective
Built and deployed a live job market intelligence platform that combines a real-time data pipeline with a dual-transport MCP server layer. The pipeline classifies daily job postings from the Adzuna and RemoteOK APIs by role, seniority, and skills into a PostgreSQL and SQLAlchemy schema, running unattended via GitHub Actions and Docker on Render. On top of that foundation, a FastMCP-based MCP server exposes five job-market analytics tools over local stdio and remote streamable HTTP, secured with bearer-token authentication and per-IP rate limiting, with a Groq-powered LLM layered on top to translate the computed statistics into a plain-English market summary grounded strictly in the underlying data.
Tools & Technologies
Project Details
Engineered a PostgreSQL and SQLAlchemy schema to classify daily job postings from the Adzuna and RemoteOK APIs by role, seniority, and skills, turning scattered real hiring data into a structured, queryable foundation.
Automated the ingestion-to-aggregation cycle with GitHub Actions and containerized the service with Docker, deploying it on Render to run continuously without manual intervention.
Refactored shared query logic into a standalone module and built a dual-transport MCP server on top of it using FastMCP, exposing five job-market analytics tools over local stdio and remote streamable HTTP mounted on the existing FastAPI app.
Secured the remote HTTP transport with bearer-token authentication and a per-IP rate limiter, and layered a Groq-powered LLM summary on top of the computed statistics to translate raw hiring trends into a plain-English narrative grounded strictly in the underlying data.