AI-Based Executive Career Platform MVP

# AI Multi-Agent Career Intelligence Platform for Executive Job Search — MVP

## Project Overview

We are looking for an experienced **AI/GenAI developer or small development team** to build an MVP of an AI-powered career intelligence platform for senior professionals and executives seeking opportunities in India and international markets.

This is **not simply a job-search scraper or resume generator**.

The objective is to build a modular multi-agent application that can discover relevant opportunities, intelligently filter and rank them, analyse Job Descriptions against a candidate's experience, create tailored resumes, prepare the candidate for interviews, and track the overall job-search journey.

The initial MVP should be kept practical and achievable, while the architecture should allow additional AI agents and capabilities to be added later.

## Phase 1 MVP Workflow

**Candidate Profile → Job Discovery → Filtering & Ranking → JD Analysis → Resume Alignment → Interview Preparation → Activity Tracking**

### 1. Candidate Profile / Career Profile

Create a structured profile containing information such as:

* Employment history
* Roles and responsibilities
* Skills
* Leadership experience
* Industries
* Projects
* Achievements
* Education and certifications
* Preferred roles
* Preferred countries/locations
* Compensation expectations
* Remote/hybrid/onsite preference
* Career objectives

The profile should become the trusted source of candidate information used by the AI.

**The AI must not invent candidate experience or achievements.**

### 2. Job Discovery Agent

Develop an agent/service capable of collecting relevant opportunities from approved sources across India and international markets.

Jobs should be converted into a common internal structure containing information such as:

**Company | Role | Location | Experience | Skills | JD | Source | Date | Application Link**

We are open to developer recommendations regarding compliant APIs, feeds and other legitimate integration approaches.

### 3. Opportunity Filtering & Matching Agent

The system should compare opportunities against candidate preferences and experience.

It should eliminate unsuitable opportunities and assign an explainable **Opportunity Match Score** to suitable jobs.

Example:

**Overall Match: 86%
Leadership Match: 92%
Technical/Functional Match: 81%
Industry Match: 85%
Location Match: 100%**

The candidate should understand *why* an opportunity has been recommended.

### 4. JD Intelligence Agent

For a selected job, the AI should analyse the Job Description and identify:

* Required skills
* Responsibilities
* Experience
* Leadership expectations
* Business requirements
* Important ATS terminology
* Candidate strengths
* Candidate gaps
* Areas that should be highlighted in the resume

The system should go beyond simple keyword matching where possible.

### 5. Resume Alignment Agent

The application should maintain a master candidate profile/resume and generate a **job-specific resume** for selected opportunities.

The AI may improve wording and positioning but must only use candidate-approved facts.

The candidate should be able to review and approve changes.

Resume versions should be retained against the corresponding job opportunity.

### 6. Interview Preparation Agent

For shortlisted opportunities, generate personalised interview preparation based on:

**Candidate Profile + Job Description + Role + Company Context**

Questions should progress through levels such as:

**Foundation → Experience → Technical/Functional → Leadership → Strategy → Complex Scenario**

The architecture should allow AI-based mock interviews to be added subsequently.

### 7. Career/Application Tracker

Provide a simple dashboard for tracking:

**Discovered → Shortlisted → Resume Prepared → Applied → Recruiter Response → Interview → Final Round → Offer → Rejected/Closed**

The candidate should be able to see their complete job-search pipeline.

Basic analytics would be useful, including applications, responses, interviews and conversion rates.

## Important Design Principle

We do **not** want an uncontrolled system automatically applying to hundreds of jobs.

The intended workflow is:

**AI discovers → AI recommends → Candidate reviews → Candidate approves → System proceeds.**

Human approval and transparency are important requirements.

## Technical Expectations

We are open to recommendations, but the developer should be comfortable with technologies such as:

* Python
* FastAPI or equivalent backend
* LLM APIs and/or suitable open-source LLMs
* Agentic AI / workflow orchestration
* LangGraph or equivalent frameworks where appropriate
* RAG and embeddings
* Vector databases
* SQL databases
* REST APIs
* Authentication and role-based access
* Structured LLM outputs
* Prompt management
* Logging and observability
* AI evaluation/testing
* Secure handling of candidate data

Please do not propose multiple autonomous agents merely for the sake of calling the solution "multi-agent."

We prefer a **reliable workflow with specialised AI capabilities and clear agent responsibilities**.

## Architecture Requirement

The MVP must be modular.

We should later be able to add capabilities such as:

* AI mock interviews
* Hard-skills and soft-skills interview agents
* Offer and compensation analysis
* Career negotiation assistance
* Professional networking intelligence
* Personal branding/content assistance
* Emerging-role and career-market intelligence
* Coach dashboard
* Alumni/community capabilities

These are **future phases and are not required for the initial MVP**.

## Security & AI Governance

Because the platform will contain confidential career information, the solution should consider:

* Candidate consent
* Secure authentication
* Role-based permissions
* Encryption
* PII protection
* Audit logs
* AI output traceability
* Human approval
* Protection against fabricated resume claims
* Appropriate retention/deletion of candidate data

## Expected Deliverables

For the MVP we expect:

1. Solution architecture
2. Data model
3. Working web application
4. Candidate profile management
5. Job discovery/integration capability
6. Opportunity matching and scoring
7. JD analysis
8. Resume tailoring
9. Interview-question generation
10. Application/career tracker
11. Basic administrative capability
12. Source code
13. API documentation
14. Deployment documentation
15. Testing
16. Technical handover

## Who We Are Looking For

Preference will be given to developers or small teams with demonstrated experience in:

**GenAI + Agentic AI + Python + LLM applications + RAG + APIs + production web applications**

Experience with HRTech, recruitment, ATS systems, job-search platforms or career applications would be an advantage.

## When Responding, Please Include

Please do not send a generic AI-generated proposal.

Instead, briefly explain:

1. How you would architect this MVP.
2. Which parts genuinely require AI agents and which should be conventional software services.
3. How you would implement job discovery without depending on unreliable scraping.
4. How you would prevent the resume agent from fabricating candidate experience.
5. How you would calculate and explain job-candidate matching.
6. Which LLM/model strategy you recommend and why.
7. Similar GenAI/agentic applications you have personally built.
8. Proposed team composition.
9. Estimated MVP development time.
10. Estimated fixed-price or milestone-based cost.
11. Recommended cloud/deployment architecture.

### Important

Please begin your proposal with the words:

**"Career Intelligence MVP"**

so we know you have read the complete requirement.

## Our Goal

We are not looking for the developer who can create the largest number of AI agents.

We are looking for someone who can help us build a **small, reliable and extensible first version of an AI Career Intelligence Platform that produces measurable value for senior professionals.**

If the MVP succeeds, there is substantial scope for subsequent development.