Full-Stack AI Engineer for Recruitment System
Budget / Salary₹12,500–37,500
TypeFreelance project
LocationRemote
Posted1 hour ago
Senior AI Architect / Full-Stack AI Engineer
Build an AI-Powered Staffing & Recruitment Operating System
We are looking for a Senior AI Architect / Full-Stack AI Engineer / AI Automation Developer, or a small expert team, to build a production-ready AI Staffing & Recruitment Operating System.
This is not a chatbot project. The goal is to build a scalable platform where approximately 40 specialized AI agents work together to automate staffing and recruitment operations.
Core Workflow
Candidate → Qualification → Job Discovery → JD Analysis → Resume Matching → Resume Optimization → Human Approval → Submission → Recruiter Communication → Interview → Offer → Placement
The platform must use:
AI Orchestrator → Specialized Agents → Tools/APIs → Event/Queue System → Database → Audit & Monitoring
Agents must support start, pause, stop, restart, retry, escalation, permissions, human approval, audit logs and cost tracking.
---
Initial MVP — 8–10 Production Agents
The first release should include:
1. Candidate Onboarding & Qualification
2. Resume Parsing/Analysis
3. Job Discovery
4. Job/JD Analysis
5. Resume–Job Matching
6. Resume Optimization/Generation
7. Recruiter Email Intelligence
8. Submission Tracking
9. Interview Tracking
10. Admin/Operations
The architecture must allow expansion to the complete 40-agent workforce without rebuilding the platform.
---
Major Capabilities
Candidate Management
Resume/profile management, skills, experience, work authorization, visa information, location, work preferences, compensation, availability, documents, RTR, submissions, interviews and offers.
Job Discovery
Integrate with permitted/authorized sources such as:
Workday, Greenhouse, Lever, iCIMS, SAP SuccessFactors, Oracle Recruiting, BambooHR, Teamtailor, Recruitee, Zoho Recruit, Darwinbox and company career sites.
Normalize jobs into a common schema and detect duplicates/expired/fraudulent jobs.
AI Matching
Generate an explainable match percentage based on:
Skills + Experience + Location + Work Authorization + Work Mode + Compensation + Education + Certifications + Industry
Default rule:
95%+ = Submission Eligible
Below threshold:
Gap Analysis → Resume Optimization → Recalculate
AI must never fabricate skills, experience, education, certifications, projects, job titles or visa information.
Recruiter Communication
Connect authorized business email, extract recruiter requirements, validate candidates, draft responses and route sensitive actions through human approval.
Interview & Offer Automation
Detect interview requests, coordinate availability, schedule interviews, send reminders, track outcomes and extract offer details.
Staffing Operations
Bench management, RTR, document collection, submissions, placements, payroll, commissions, contracts, invoices and compliance.
Financial calculations must be performed by deterministic software, not AI.
---
Human Approval & Security
Create a centralized approval queue for:
- Resume submission
- Job submission
- RTR
- Rate confirmation
- Sensitive documents
- Interview responses
- Offers
- Contracts
- Placements
Implement:
RBAC/ABAC, MFA, SSO, encryption, secrets management, audit logging, consent, data retention/deletion, secure document storage, access logging, backups and disaster recovery.
Sensitive data must be logically separated, including:
Candidate PII | Documents | Financial Data | Company Data | AI Logs
---
AI Command Center
Provide a dashboard showing:
- Agent status
- Current task
- Success/failure
- Errors
- Human approvals
- Token usage
- AI cost
- Performance
- Permissions
- Daily budgets
Controls:
START | PAUSE | STOP | RESTART | RETRY | LOGS
---
Recommended Stack
Frontend: Next.js + TypeScript
Backend: Node.js/TypeScript + Python/FastAPI
Database: PostgreSQL/Supabase
Auth: Supabase Auth/Enterprise IdP + MFA/SSO
AI: OpenAI + Claude + Gemini abstraction layer
Events/Queues: Redis + durable event system
Vector Search: pgvector
Search: PostgreSQL initially, OpenSearch when required
Storage: Encrypted S3-compatible storage
Payments: Stripe/Razorpay
Integrations: ATS, Email, Calendar, E-signature and authorized job APIs
---
Development Roadmap
Phase 1: 8–10 production agents + core platform
Phase 2: Staffing automation, interviews, offers and placements
Phase 3: Full 40-agent AI workforce
Phase 4: Global AI Job Search & Recruitment Marketplace
Long-term support should include USA, India, UK, EU and other countries, with multi-currency, multi-language and configurable country-specific workflows.
---
Acceptance Test
The MVP must successfully demonstrate:
Create Candidate → Upload Resume → Parse → Import Job → Analyze JD → Calculate Match → Explain Score → Optimize Resume → Human Approval → Submit → Capture Recruiter Response → Detect Interview → Schedule → Track → Extract Offer → Create Placement → Audit Everything
---
Required Experience
Must Have
- Production AI agents
- Multi-agent orchestration
- LLM applications
- Python/FastAPI
- Node.js/TypeScript
- Next.js
- PostgreSQL/Supabase
- APIs
- Event-driven architecture
- Redis/background jobs
- Authentication/RBAC
- Cloud deployment
- Git/GitHub
Preferred
OpenAI, Claude, Gemini, LangGraph, RAG, pgvector, OpenSearch, Workday, Greenhouse, Lever, iCIMS, SAP SuccessFactors, Oracle Recruiting, email/calendar APIs, Stripe/Razorpay, OCR and recruitment/staffing platforms.
Applicants without production AI-agent experience should not apply.
---
Proposal Requirements
Please provide:
1. 3–5 relevant production projects
2. Proposed architecture for scaling 8–10 agents to 40
3. Agent orchestration strategy
4. AI model/vendor-lock-in strategy
5. Resume matching methodology
6. Security approach
7. Human approval design
8. Recommended technology stack
9. MVP and full-project timeline
10. Development cost, team size and ongoing maintenance estimate
Key Question
How would you build the first 8–10 production AI agents so they can scale into a reliable 40-agent AI Staffing Operating System without rebuilding the core platform?
We are looking for a team capable of taking the project from:
Architecture → UI/UX → Development → AI Agents → Integrations → Security → Testing → Deployment → Production
The ultimate goal is to build a Global AI Job Search + AI Recruitment Marketplace, not a prototype chatbot.
Build an AI-Powered Staffing & Recruitment Operating System
We are looking for a Senior AI Architect / Full-Stack AI Engineer / AI Automation Developer, or a small expert team, to build a production-ready AI Staffing & Recruitment Operating System.
This is not a chatbot project. The goal is to build a scalable platform where approximately 40 specialized AI agents work together to automate staffing and recruitment operations.
Core Workflow
Candidate → Qualification → Job Discovery → JD Analysis → Resume Matching → Resume Optimization → Human Approval → Submission → Recruiter Communication → Interview → Offer → Placement
The platform must use:
AI Orchestrator → Specialized Agents → Tools/APIs → Event/Queue System → Database → Audit & Monitoring
Agents must support start, pause, stop, restart, retry, escalation, permissions, human approval, audit logs and cost tracking.
---
Initial MVP — 8–10 Production Agents
The first release should include:
1. Candidate Onboarding & Qualification
2. Resume Parsing/Analysis
3. Job Discovery
4. Job/JD Analysis
5. Resume–Job Matching
6. Resume Optimization/Generation
7. Recruiter Email Intelligence
8. Submission Tracking
9. Interview Tracking
10. Admin/Operations
The architecture must allow expansion to the complete 40-agent workforce without rebuilding the platform.
---
Major Capabilities
Candidate Management
Resume/profile management, skills, experience, work authorization, visa information, location, work preferences, compensation, availability, documents, RTR, submissions, interviews and offers.
Job Discovery
Integrate with permitted/authorized sources such as:
Workday, Greenhouse, Lever, iCIMS, SAP SuccessFactors, Oracle Recruiting, BambooHR, Teamtailor, Recruitee, Zoho Recruit, Darwinbox and company career sites.
Normalize jobs into a common schema and detect duplicates/expired/fraudulent jobs.
AI Matching
Generate an explainable match percentage based on:
Skills + Experience + Location + Work Authorization + Work Mode + Compensation + Education + Certifications + Industry
Default rule:
95%+ = Submission Eligible
Below threshold:
Gap Analysis → Resume Optimization → Recalculate
AI must never fabricate skills, experience, education, certifications, projects, job titles or visa information.
Recruiter Communication
Connect authorized business email, extract recruiter requirements, validate candidates, draft responses and route sensitive actions through human approval.
Interview & Offer Automation
Detect interview requests, coordinate availability, schedule interviews, send reminders, track outcomes and extract offer details.
Staffing Operations
Bench management, RTR, document collection, submissions, placements, payroll, commissions, contracts, invoices and compliance.
Financial calculations must be performed by deterministic software, not AI.
---
Human Approval & Security
Create a centralized approval queue for:
- Resume submission
- Job submission
- RTR
- Rate confirmation
- Sensitive documents
- Interview responses
- Offers
- Contracts
- Placements
Implement:
RBAC/ABAC, MFA, SSO, encryption, secrets management, audit logging, consent, data retention/deletion, secure document storage, access logging, backups and disaster recovery.
Sensitive data must be logically separated, including:
Candidate PII | Documents | Financial Data | Company Data | AI Logs
---
AI Command Center
Provide a dashboard showing:
- Agent status
- Current task
- Success/failure
- Errors
- Human approvals
- Token usage
- AI cost
- Performance
- Permissions
- Daily budgets
Controls:
START | PAUSE | STOP | RESTART | RETRY | LOGS
---
Recommended Stack
Frontend: Next.js + TypeScript
Backend: Node.js/TypeScript + Python/FastAPI
Database: PostgreSQL/Supabase
Auth: Supabase Auth/Enterprise IdP + MFA/SSO
AI: OpenAI + Claude + Gemini abstraction layer
Events/Queues: Redis + durable event system
Vector Search: pgvector
Search: PostgreSQL initially, OpenSearch when required
Storage: Encrypted S3-compatible storage
Payments: Stripe/Razorpay
Integrations: ATS, Email, Calendar, E-signature and authorized job APIs
---
Development Roadmap
Phase 1: 8–10 production agents + core platform
Phase 2: Staffing automation, interviews, offers and placements
Phase 3: Full 40-agent AI workforce
Phase 4: Global AI Job Search & Recruitment Marketplace
Long-term support should include USA, India, UK, EU and other countries, with multi-currency, multi-language and configurable country-specific workflows.
---
Acceptance Test
The MVP must successfully demonstrate:
Create Candidate → Upload Resume → Parse → Import Job → Analyze JD → Calculate Match → Explain Score → Optimize Resume → Human Approval → Submit → Capture Recruiter Response → Detect Interview → Schedule → Track → Extract Offer → Create Placement → Audit Everything
---
Required Experience
Must Have
- Production AI agents
- Multi-agent orchestration
- LLM applications
- Python/FastAPI
- Node.js/TypeScript
- Next.js
- PostgreSQL/Supabase
- APIs
- Event-driven architecture
- Redis/background jobs
- Authentication/RBAC
- Cloud deployment
- Git/GitHub
Preferred
OpenAI, Claude, Gemini, LangGraph, RAG, pgvector, OpenSearch, Workday, Greenhouse, Lever, iCIMS, SAP SuccessFactors, Oracle Recruiting, email/calendar APIs, Stripe/Razorpay, OCR and recruitment/staffing platforms.
Applicants without production AI-agent experience should not apply.
---
Proposal Requirements
Please provide:
1. 3–5 relevant production projects
2. Proposed architecture for scaling 8–10 agents to 40
3. Agent orchestration strategy
4. AI model/vendor-lock-in strategy
5. Resume matching methodology
6. Security approach
7. Human approval design
8. Recommended technology stack
9. MVP and full-project timeline
10. Development cost, team size and ongoing maintenance estimate
Key Question
How would you build the first 8–10 production AI agents so they can scale into a reliable 40-agent AI Staffing Operating System without rebuilding the core platform?
We are looking for a team capable of taking the project from:
Architecture → UI/UX → Development → AI Agents → Integrations → Security → Testing → Deployment → Production
The ultimate goal is to build a Global AI Job Search + AI Recruitment Marketplace, not a prototype chatbot.
Apply on Freelancer →
Project sourced from Freelancer.com. Applications happen directly on the original platform — we never collect your data.