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🚀 AI-Powered Job Discovery & Career Copilot

An Enterprise-Grade, Multi-Agent AI Platform for Autonomous Career Management

Python 3.14 Next.js 16 FastAPI Temporal PostgreSQL Docker Terraform

🌟 Overview

This platform is a state-of-the-art AI Career Copilot designed to demonstrate advanced software engineering, distributed systems, and AI agent orchestration. It autonomously discovers, scores, and manages job opportunities using a hyper-personalized professional profile.

Built with an uncompromising focus on scalability, security, and Twelve-Factor App principles, this project reflects senior-level engineering practices ready for production environments.

🎯 Key Capabilities & Business Value

  • 🧠 Autonomous Multi-Agent Swarm: A decentralized architecture of 8 specialized AI agents (Scraping, Ranking, RAG, Q&A, Cover Letter, Orchestrator, Observability, and Security) working in tandem via Temporal workflows.
  • 🎯 Semantic Matching (RAG): Uses pgvector and deep semantic search to rank job descriptions against candidate CVs with high precision, removing the noise of traditional keyword matching.
  • 🛡️ Enterprise Security: Built-in OWASP hardening, Supabase JWT authentication, Role-Based Access Control (RBAC), Row-Level Security (RLS), and active prompt injection defenses.
  • 📊 Deep Observability: Comprehensive telemetry with OpenTelemetry, Prometheus, Loki, Grafana, and Sentry for real-time monitoring of AI hallucinations, token budgets, and system latency.
  • ☁️ Cloud-Native Infrastructure: Infrastructure as Code (IaC) via Terraform, supporting multi-cloud deployments to Azure Container Apps and AWS ECS Fargate, fully containerized with Docker.

🛠️ Technology Stack (2026 Modern Standard)

Frontend (User Experience)

  • Framework: Next.js 16 (App Router, Static Export), React 19
  • Styling: Tailwind CSS v4, Modern Flexbox & Grid, Responsive Design
  • State Management: TanStack Query (React Query) featuring high-performance Keyset (Cursor-based) Pagination capable of scaling to millions of rows.

Backend & AI (Core Logic)

  • Language & Framework: Python 3.14, FastAPI (Async), uv package manager
  • AI Engine: Multi-Agent Architecture, RAG, Prompt Engineering, Strict XML Prompt Contracts
  • Web Scraping: Playwright (Headless browser automation), BeautifulSoup
  • Orchestration: Temporal for distributed, fault-tolerant workflow management and dead-letter queues.

Database, Caching & Data Engineering

  • Primary Database: PostgreSQL (Supabase) with asyncpg
  • Vector Search: pgvector for localized AI embeddings
  • Caching & Rate Limiting: Redis (aioredis) with sliding window algorithms
  • Schema Migrations: Alembic

DevOps & Infrastructure

  • CI/CD: GitHub Actions (Linting, Ragas/DeepEval Regression testing, Terraform pipelines)
  • Containerization: Docker multi-stage builds, Nginx reverse proxy, Supervisord
  • Security: Cosign (Docker Image Signing), Bandit, Trivy

🧠 The AI Agent Swarm

The system is powered by a decentralized swarm of AI agents, each governed by strict XML contracts and isolated responsibilities:

Agent Responsibility
Scraping Agents Navigates volatile session URLs on LinkedIn/JobServe to extract and standardize data.
Ranking Agent Scores jobs against candidate CVs using cross-encoder confidence intervals.
RAG Agent Contextualizes job descriptions with candidate history via vector search.
Q&A Agent Provides grounded, hallucination-free interactive answers about job postings.
Cover Letter Agent Dynamically generates ATS-optimized, personalized cover letters.
Orchestrator Agent Manages long-running workflows, circuit breakers, and retries via Temporal.
Observability Agent Monitors AI schema conformance, tracks token budgets, and alerts on anomalies.
Security Agent Sanitizes user inputs and actively guards against prompt injections.

🚀 Getting Started

Prerequisites

  • Docker and Docker Compose
  • Node.js 22+
  • Python 3.14+ and uv package manager

Option A: Production Containerized Environment (Recommended)

The entire stack is orchestrated via Docker Compose for a seamless setup.

Note: An .env file must exist in the root directory before running Docker. Copy .env.example to .env and fill in your API keys and configuration.

cp .env.example .env
# Edit .env with your credentials
docker compose up -d --build

The unified application is now accessible at: http://localhost

Option B: Native Development (Hot-Reload)

1. Start the Backend (FastAPI)

cd backend
uv sync
uv run --project backend uvicorn backend.main:app --reload --port 8000

API Docs: http://localhost:8000/api/docs

2. Start the Frontend (Next.js)

cd frontend
npm install
npm run dev

Dashboard: http://localhost:3000


📜 Engineering Standards & Culture

This project strictly adheres to Twelve-Factor App (2026) principles, demonstrating a mature engineering mindset:

  • Documentation Driven: Every module and agent contains localized AGENT.md guidelines outlining input/output contracts.
  • Reliability First: Integrated DIFA (Discover, Isolate, Fix, Assess) framework and ReAct loop execution protocols.
  • Test-Driven AI: Prompt regression testing integrated directly into the CI pipeline using DeepEval and Ragas.
  • Zero Magic: Explicit over implicit. No "pseudo-code" allowed in production—every feature is fully implemented and tested.

Designed and developed by Qasir Mehmood to push the boundaries of AI-driven applications.

About

AI Career Copilot monorepo — production 8-agent architecture on Temporal: scrape → RAG rank → ATS cover letters. Next.js 16 · React 19 · FastAPI · Playwright · pgvector · LiteLLM · OTel · Terraform.

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