Full-Stack AI Developer for Analytics Dashboard

Need: Full-Stack Developer for AI Data Ingestion & Analytics Dashboard (Phase 1 MVP)

Project Overview
We are launching an adaptive AI intelligence platform designed to follow an individual’s professional journey. We are skipping static design mockups and building a functional, code-first Phase 1 MVP to present to institutional investors.

The core objective of this MVP is to demonstrate a seamless, intelligent Live Data Transformation Flow: taking raw, messy user data and instantly turning it into a structured database asset that generates dynamic career analytics.

Target Tech Stack (Open to Recommendations)
Backend: Python (FastAPI / Django) or Node.js

Database: PostgreSQL or MongoDB

AI Integration: OpenAI API (utilizing Structured Outputs / JSON Mode)

Frontend: React, Vue.js, or a rapid-development web wrapper (e.g., Bubble/Softr) utilizing custom code blocks for backend communication.

Core Features & Scope of Work (Phase 1 MVP)
1. Dynamic Onboarding "Fork" Screen
A clean, conversational entry page with a high-conviction welcome message:

"Welcome. You've just decided to take destiny into your own hands. Let's Rezume."

The system must segment users into two distinct pathways:

Pathway A ("Find a new job" / "Switch career paths"): Directs users to an instant drag-and-drop file ingestion zone.

Pathway B ("Find my first job / I'm a student"): Bypasses file upload and redirects to a conversational AI text-input wizard to collect academic majors, key projects, and career goals.

2. PDF Ingestion & Strict JSON Schema Parsing (The "Brain")
Build a robust pipeline that extracts text from uploaded PDF resumes, CVs, or LinkedIn profile printouts.

The raw text must be piped to the OpenAI API using strict JSON schemas to guarantee the database always receives clean, un-hallucinated, structured objects mapping:

Core Technical & Soft Skills

Granular Work/Academic History

Implicit Career Trajectory

3. Investor Dashboard View (The Analytics Engine)
Once parsed, the web app must instantly load a dashboard showing three dynamic modules fueled by the AI schema data:

Profile Summary: A clean, structural visualization of the user's data asset (The Personal Twin).

Career Health Score: A dynamic numerical metric (e.g., 78/100) based on market demand for their current skill cluster.

Skill Gap Analysis: A targeted side-by-side visualization highlighting exactly what skills or certifications the user is missing to unlock the next professional tier or change paths.

Timeline & Milestone Schedule
This project is scoped strictly for 4 to 6 weeks of execution with an estimated workload of 120–150 billable hours.

Payments will be rigidly bound to the following milestones:

Milestone 1 (Paid Test - 48 Hours): Parse a single messy test PDF resume and successfully return a clean, validated JSON object via the OpenAI API.

Milestone 2 (Week 1-2): Database architecture configured, API pipelines locked in, and Onboarding screen flows functional.

Milestone 3 (Week 3-4): Integration of the backend data with the frontend dashboard UI components (Health Score & Gap Analysis layout rendering live data).

Milestone 4 (Week 5-6): Bug scrubbing, speed optimization for live demos, staging deployment, and final source code handoff.

Screening Requirements (To Apply)
To filter out generic agencies and unqualified applicants, you must include the following in your proposal:

A brief link to a live web application or GitHub repository where you personally built an LLM pipeline that extracts unstructured data and structures it into a strict database schema.

Your preferred backend stack for executing this quickly without sacrificing data integrity.

Daily updates will be sent at the end of each business day in your time zone.

Phase 1: Core JSON Schema:

{
"personal_twin": {
"metadata": {
"parsing_confidence_score": 0.95,
"last_updated": "2026-06-10"
},
"profile": {
"full_name": "String",
"current_role": "String",
"target_role": "String",
"years_of_experience": "Float"
},
"experience_history": [
{
"company_name": "String",
"job_title": "String",
"duration_months": "Integer",
"core_responsibilities": ["String"],
"implicit_skills_demonstrated": ["String"]
}
],
"skills_inventory": {
"technical_skills": ["String"],
"soft_skills": ["String"],
"tools_and_software": ["String"]
},
"ai_analytics": {
"career_health_score": "Integer (1-100)",
"score_justification": "String",
"identified_skill_gaps": [
{
"missing_skill": "String",
"priority_level": "String (High/Medium/Low)",
"reason": "String"
}
],
"recommended_next_steps": ["String"]
}
}
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