AI-Driven Hospitality Hiring Platform MVP
Budget: €10,000 – €20,000 EUR
Project Specification: AI-Powered Hospitality Hiring MVP Development
We are seeking an experienced AI/ML engineer or a small development team to build an MVP for an AI-powered hiring platform tailored for the hospitality industry. The goal is to automate candidate screening, scheduling, and AI-led interviews while integrating predictive analytics to help hotels hire more efficiently and reduce bias in the process.
Scope of Work
The MVP should include the following core features:
1. AI-Powered CV Ranking & Candidate Evaluation
Smart CV Parsing & Ranking
Extracts key details from PDF/Word resumes (experience, skills, tenure and average tenure, certifications).
Assigns a Job Match Score based on predefined hiring criteria.
Weights hospitality-specific skills, experience, and job stability.
Work Eligibility & Risk Flagging
Detects visa status and work authorization.
Flags location risks (e.g., long commute distances).
Identifies anomalies (foreign phone number vs. claimed local presence).
Automated Background & Risk Assessment
Performs basic web searches (Google, LinkedIn, relevant databases) to flag potential concerns.
Scans for criminal record indicators (where legally permissible).
2. 24/7 AI-Driven Scheduling & Candidate Engagement
Omnichannel Interview Scheduling
Text-based and email based scheduling via SMS/WhatsApp/outlook with automated reminders.
Intelligent Follow-Ups & Candidate Engagement
Sends automated follow-ups for no-shows.
Adjusts scheduling based on candidate availability.
Integrates into existing ATS (if required in future iterations).
3. AI Screening Interviews & Candidate Assessment
Two-Way AI Video & Voice Interviews done via mobile/teams/zoom/google meet
AI conducts and records video or voice interviews.
Asks personalized screening questions based on CV data.
Follows up dynamically based on responses for a conversational feel.
Role-Specific Scenario Testing & Language Proficiency
Presents hospitality-specific scenarios (handling guest complaints, multitasking) based on the job title and job description
Evaluates problem-solving, emotional intelligence, and adaptability.
Analyzes English language proficiency (fluency, tone, comprehension).
4. AI-Powered Candidate Insights & Dashboard
Candidate Scoring & Shortlisting
Generates automated ranking lists based on job match, communication ability, and customer service potential.
Produces AI-generated summaries for hiring managers, eliminating the need to review lengthy applications.
Candidate Pool Analytics & Benchmarking
Provides real-time insights into hiring trends, application volume, and quality of applicants.
Allows hiring managers to compare candidates instantly with industry benchmarks.
Technology Requirements & Deliverables
Tech Stack: Open to recommendations (AWS/GCP, Python, React, or Node.js preferred).
Machine Learning Models: NLP for CV parsing & ranking, Speech Analysis for interview assessment.
Database: Cloud-based storage for candidate profiles & interview recordings.
Deliverables: Fully functional MVP prototype, deployed on a cloud server with API access.
Timeframe: Ideally 4-6 weeks
Please include:
Estimated development cost for MVP.
Tech stack recommendation & past relevant projects.
Development timeline & phased deliverables.
Approach to AI models & integrations.
We are seeking an experienced AI/ML engineer or a small development team to build an MVP for an AI-powered hiring platform tailored for the hospitality industry. The goal is to automate candidate screening, scheduling, and AI-led interviews while integrating predictive analytics to help hotels hire more efficiently and reduce bias in the process.
Scope of Work
The MVP should include the following core features:
1. AI-Powered CV Ranking & Candidate Evaluation
Smart CV Parsing & Ranking
Extracts key details from PDF/Word resumes (experience, skills, tenure and average tenure, certifications).
Assigns a Job Match Score based on predefined hiring criteria.
Weights hospitality-specific skills, experience, and job stability.
Work Eligibility & Risk Flagging
Detects visa status and work authorization.
Flags location risks (e.g., long commute distances).
Identifies anomalies (foreign phone number vs. claimed local presence).
Automated Background & Risk Assessment
Performs basic web searches (Google, LinkedIn, relevant databases) to flag potential concerns.
Scans for criminal record indicators (where legally permissible).
2. 24/7 AI-Driven Scheduling & Candidate Engagement
Omnichannel Interview Scheduling
Text-based and email based scheduling via SMS/WhatsApp/outlook with automated reminders.
Intelligent Follow-Ups & Candidate Engagement
Sends automated follow-ups for no-shows.
Adjusts scheduling based on candidate availability.
Integrates into existing ATS (if required in future iterations).
3. AI Screening Interviews & Candidate Assessment
Two-Way AI Video & Voice Interviews done via mobile/teams/zoom/google meet
AI conducts and records video or voice interviews.
Asks personalized screening questions based on CV data.
Follows up dynamically based on responses for a conversational feel.
Role-Specific Scenario Testing & Language Proficiency
Presents hospitality-specific scenarios (handling guest complaints, multitasking) based on the job title and job description
Evaluates problem-solving, emotional intelligence, and adaptability.
Analyzes English language proficiency (fluency, tone, comprehension).
4. AI-Powered Candidate Insights & Dashboard
Candidate Scoring & Shortlisting
Generates automated ranking lists based on job match, communication ability, and customer service potential.
Produces AI-generated summaries for hiring managers, eliminating the need to review lengthy applications.
Candidate Pool Analytics & Benchmarking
Provides real-time insights into hiring trends, application volume, and quality of applicants.
Allows hiring managers to compare candidates instantly with industry benchmarks.
Technology Requirements & Deliverables
Tech Stack: Open to recommendations (AWS/GCP, Python, React, or Node.js preferred).
Machine Learning Models: NLP for CV parsing & ranking, Speech Analysis for interview assessment.
Database: Cloud-based storage for candidate profiles & interview recordings.
Deliverables: Fully functional MVP prototype, deployed on a cloud server with API access.
Timeframe: Ideally 4-6 weeks
Please include:
Estimated development cost for MVP.
Tech stack recommendation & past relevant projects.
Development timeline & phased deliverables.
Approach to AI models & integrations.