ai-career-opportunity-assistant

🚀 AI Career Opportunity Assistant

An intelligent, workflow-driven system designed to automate the extraction, evaluation, and filtering of job and internship opportunities from email data using AI-based decision-making.


📌 Overview

In the modern job ecosystem, students and fresh graduates are exposed to a large volume of opportunity-related emails from multiple platforms. These emails are often unstructured, repetitive, and misaligned with individual preferences, making manual filtering inefficient and error-prone.

This project addresses the problem by developing an AI-powered automation pipeline that transforms raw email content into structured, actionable insights. The system not only identifies valid opportunities but also evaluates their relevance based on a personalized user profile.


🎯 Objective


⚙️ System Architecture

The system follows a multi-stage pipeline:

Email Trigger → Validation → AI Agent 1 → Conditional Filtering → AI Agent 2 → Decision Engine → Google Sheets Storage

Each stage is designed to progressively refine raw input into meaningful output.


🧠 Core System Logic

🔹 Stage 1: Email Acquisition

The workflow is triggered using a Gmail integration, which continuously monitors incoming emails and captures their content for processing.


🔹 Stage 2: Input Validation

An initial validation layer ensures that empty or invalid emails are filtered out before entering the pipeline. This prevents unnecessary computation and API usage.


🔹 Stage 3: AI Agent 1 — Data Extraction

The first AI agent is responsible for analyzing raw email content and extracting structured job-related information.

Key Responsibilities:

Important Behavior:


🔹 Stage 4: Conditional Filtering

A logical condition evaluates whether the extracted content qualifies as a job or internship opportunity.


🔹 Stage 5: AI Agent 2 — Relevance Evaluation

The second AI agent performs personalized evaluation using a predefined user profile.

User Profile Includes:

Evaluation Logic:


🔹 Stage 6: Decision Engine

Based on the evaluation output:


🔹 Stage 7: Data Storage

All processed data is stored in Google Sheets in a structured format, enabling easy tracking, filtering, and future analysis.


📸 System Implementation (Visual Evidence)

🔹 Workflow Implementation (n8n)

Workflow

This represents the actual automation pipeline built using n8n. It illustrates the integration of triggers, AI agents, conditional nodes, and storage mechanisms into a unified system.


🔹 Workflow Logic Flowchart

Flowchart

The flowchart provides a conceptual representation of the system logic, outlining how data moves through each stage of processing and decision-making.


🔹 Selected Opportunities Output

Selected

This dataset contains opportunities that meet the defined user criteria. Each entry includes structured attributes along with relevance indicators such as match score and decision status.


🔹 Rejected Opportunities Output

Rejected

This dataset captures opportunities that were identified as job-related but did not satisfy the relevance conditions. Maintaining this dataset ensures transparency and traceability in decision-making.


🔹 Lean Canvas (Product Perspective)

Lean Canvas

The lean canvas outlines the problem space, solution approach, user segments, and value proposition, reflecting the product thinking behind the system design.


🧪 Edge Case Handling

The system has been tested against multiple real-world scenarios to ensure robustness:


🛠️ Technology Stack


📊 Results & Impact


⚠️ Limitations


🔮 Future Scope


👤 Author

Tripti Kumari
B.Tech Computer Science Engineering
Narula Institute of Technology


⭐ Final Note

This project demonstrates the practical application of AI and workflow automation in solving real-world problems. By combining structured data extraction with intelligent evaluation, the system transforms unorganized inputs into meaningful, decision-ready outputs.