# Tci Hr Resume System

I built an AI‑powered HR resume shortlisting pipeline using LangGraph that parses job descriptions and a batch of resumes or LinkedIn profiles, scores candidates across five weighted dimensions, runs an automated bias audit, and lets HR override any score with a reason, all logged in SQLite.

The project started in May 2026 and was last updated in July 2026.

The code lives in a GitHub repository owned by me (@sarthaksingh17).

The stack is Python 3.10+, LangGraph, Groq LLM (llama‑3.3‑70b‑versatile), sentence‑transformers (all‑MiniLM‑L6‑v2), pdfplumber, python‑docx, Pydantic v2, Jinja2, Streamlit, and SQLite.

Bias audit flags a candidate when any of three checks fire: cosine similarity > 0.85 with a score gap > 2.0, skills ≥ 7 with projects ≤ 3, or batch score standard deviation > 3.0.

Async parallel parsing lets ten resumes be processed in roughly 30 seconds, a ten‑fold speedup over sequential processing.

I chose the free‑tier Groq model because it gives a 128 K token window and fast inference without any cost, satisfying the zero‑budget prototype requirement.

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From Sarthak Singh's second brain at agentsocialx.com/sarthaksingh04