Built for SUDHEE 2026 – CBIT Hackathon
Alpha Coders is an AI-driven candidate evaluation platform that ranks students based on real technical signals extracted from:
- Coding platforms (LeetCode)
- Development platforms (GitHub)
- Professional presence (LinkedIn)
- Resume content
Instead of relying solely on static resumes, the system uses Natural Language Processing (NLP), semantic embeddings, and weighted scoring algorithms to evaluate real-world technical competency and match candidates to a given job description.
Modern hiring pipelines face several challenges:
- Resume keyword stuffing
- Poor validation of practical skills
- Manual shortlisting bias
- No structured evaluation of GitHub or LeetCode activity
- Over-reliance on resume formatting
As a result, strong candidates are often overlooked due to weak keyword alignment or presentation issues.
Alpha Coders introduces an intelligent ranking engine that:
- Extracts required skills from job descriptions using NLP
- Converts candidate profiles into semantic embeddings
- Computes similarity between job vectors and candidate vectors
- Applies platform-wise weighted scoring
- Generates an objective ranked shortlist
- Provides explainable breakdown of scores
- Candidate database (LeetCode, GitHub, LinkedIn, Resume)
- Recruiter-provided job description
- NLP-based keyword extraction
- Technical skill normalization
- Domain classification
- Convert job description into vector embeddings
- Convert candidate profiles into vector embeddings
- Store embeddings inside MongoDB
- Cosine similarity computation
- Platform-weighted scoring
- Skill gap identification
- Composite final score calculation
- Ranked output (Most suitable → Least suitable)
- Explainable score breakdown
Frontend (HTML, CSS, JavaScript)
↓
Backend API (Python)
↓
Skill Extraction & Embedding Engine
↓
MongoDB (Candidate Data + Stored Embeddings)
↓
Ranking & Scoring Module
- Python
- FastAPI
- Vector Embeddings
- REST APIs
- HTML
- CSS
- JavaScript
- MongoDB
- Pre-computed candidate embeddings
- Multi-platform skill aggregation
- Embedding-based semantic matching
- Customizable weighted scoring system
- Explainable AI ranking
- Bias-reduced candidate screening
- Skill gap analysis for students
- Recruiter-friendly ranking dashboard
Final Score =
(LeetCode Performance × Weight₁)
+ (GitHub Activity × Weight₂)
+ (LinkedIn Skill Match × Weight₃)
+ (Resume Keyword Match × Weight₄)
+ (Embedding Similarity Score × Weight₅)
Each platform contributes differently based on recruiter-defined importance.
Placement coordinator can upload:
- GitHub username
- LeetCode username
- LinkedIn PDF
- Resume PDF
System extracts structured + unstructured skills.
Placement coordinator can upload:
- Excel (.xlsx)
- JSON (.json)
Containing: name, branch, year, skills, github_username, leetcode_username
Recruiter Input:
"Looking for a MERN stack developer with strong DSA and backend skills."
System Process:
- Extract MERN, DSA, Backend as skill vectors
- Convert job description into semantic embedding
- Match against all candidate embeddings
- Compute similarity + weighted scores
- Rank candidates by relevance
- Highlight strong and missing skills
- Siddhi Sritha Shetkar – Team Lead | Frontend & UI
- Ailapuram SaiShloka Reddy – Backend & Database Systems
- Sanjana Donthireddy – AI & Matching Engine
- Live API integration with GitHub & LeetCode
- LLM-powered skill inference
- Advanced recruiter analytics dashboard
- Candidate performance trend visualization
- Bias detection & fairness auditing module
- Real-time recruiter feedback learning loop