AI-Assisted HR Recruitment Bot

@mussaibb7

I'll deliver a robust AI-Assisted HR Recruitment Bot leveraging Python, FastAPI, and seamless integrations. The core value lies in a clean, maintainable backend structure that scales under real-world usage, not just a quick proof of concept. Building on my experience with multi-client ML inference APIs, I've successfully deployed scalable, low-latency solutions (sub-200ms latency) with production-grade cloud delivery. For instance, my Multi-Client ML Inference API showcases my ability to handle multiple clients, and the GPT-4 Clinical Document Automation Pipeline demonstrates my expertise in integrating AI models with FastAPI. To ensure successful delivery, I'll work closely with you to clarify the scope, first milestone, and most critical technical constraints. A possible execution plan involves delivering clean backend code, environment setup instructions, error handling, and API integration flow documentation. Do you already have the API contracts and environment ready, or should I define that structure first?

@ravikumarsingh1

I can build the AI-Assisted Recruitment Bot described in your requirements using a cost-effective Google Workspace-based architecture. Proposed Stack: • Google Apps Script • Gmail Integration • Google Sheets • Gemini/OpenAI API • Lightweight recruiter dashboard Key Features: • Automatic Gmail monitoring and application classification • Resume parsing (PDF/DOCX) • LinkedIn, portfolio, and website extraction • Duplicate and multi-post application tracking • AI-powered CV scoring and recommendations • Dynamic candidate filtering by salary, distance, experience, qualification, and custom fields • Multi-tier screening pools and backup candidate lists • Automated recruitment workflow: • Acknowledgment emails • Candidate form collection • Interview scheduling • Recruiter status updates • Historical validation across recruitment cycles • Excel/CSV export support • DPDPA-conscious data handling Deliverables: • Fully functional recruitment system • Source code and deployment support • Documentation and user guide • Testing and demonstration • Historical validation and data-retention procedures The proposed architecture is designed specifically for low operational cost and occasional recruitment cycles while remaining scalable for future growth.

@apoorvar16

We can build your AI-Assisted Recruitment & CV Screening Bot as a low-cost, scalable automation system using Google Apps Script + Gmail + Google Sheets + Gemini/OpenAI API. Solution will include: * Gmail monitoring for incoming CVs (PDF/DOCX parsing) * AI-based resume extraction (name, skills, experience, qualification, salary, location) * Domain classification (Teaching / Non-Teaching / Admin) * Link extraction (LinkedIn, portfolio, websites + AI summary) * Duplicate + multi-role candidate tracking without data overwrite * AI scoring & multi-tier shortlisting (primary + backup pools) * Google Sheets database with structured candidate records * Export options (CSV/XLSX) * Dynamic filtering dashboard (salary, distance, experience ranges) * Automated email workflow: 1. Acknowledgement mail 2. Shortlist form submission request 3. Interview scheduling with accept/decline tracking 4. Recruiter status updates (Selected/Rejected/Demo/etc.) * Historical cycle validation (past applicants auto-classified) * DPDPA-compliant secure data handling Approach is lightweight (serverless-first), ensuring minimal maintenance and cost since usage is only 4–5 cycles/year. Delivery timeline: 3–5 weeks Support: deployment + documentation included

@Sahilh77

Hello, This project closely matches a recruitment automation system I have already developed. I built an AI-assisted recruitment platform that automated resume parsing, candidate screening, duplicate detection, email workflows, candidate tracking, interview management, and recruiter reporting. I can develop a cost-effective solution that monitors recruitment emails, extracts and analyzes CVs, identifies duplicate and multi-position applications, stores candidate information, performs AI-based screening against configurable job criteria, and manages the complete recruitment workflow from application to final selection. The system will support automated candidate communication, interview scheduling, dynamic filtering, historical candidate validation, reporting, and Google Sheets/Excel exports while remaining simple to maintain and scale. My experience includes AI automation, workflow systems, dashboard development, document processing, FastAPI applications, and recruitment-related automation. This allows me to deliver a practical solution that aligns with your requirements rather than a generic AI implementation. I can provide the complete source code, deployment support, documentation, testing, and future enhancement support. Available to start immediately. Best regards, Sahil

@rakhshanz3

With my strong academic background in Artificial Intelligence and nearly 20 years of diverse industry experience, I am confident that I have the expertise needed to successfully execute this AI-Assisted Recruitment and CV Scr In conclusion, I bring not only technical acumen but also a strategic mindset. As part of any project development process, I prioritize understanding the unique needs and translating them into practical solutions. With PhD in AI and significant professional experience in academia as well as software engineering –including roles involving such organizational challenges - I strongly believe I can contribute significantly towards making your recruitment processes efficient and increasingly automated. Don't hesitate to reach out!

@thetechnicalhunt

The hard part here is not just parsing CVs. It is making the recruitment workflow deterministic enough that duplicate detection, role pools, interview stages, and DPDPA cleanup stay reliable while the LLM only handles the ambiguous resume reasoning. I have 15 years building production software and AI systems as a TeamOfOne. At Fold Health I ran LLM/RAG and workflow systems at production scale, where structured extraction, auditability, and human handoff mattered because the input was messy and sensitive. For this I would keep the architecture low-cost and maintainable: Gmail trigger plus Apps Script or a lightweight Python/Cloud Run worker, Sheets as the first operational database, deterministic rules for dedupe/status/stage transitions, and an LLM layer for CV summarization, role-fit scoring, salary/distance/experience extraction, and secondary candidate pools. The dashboard/config page would control ranges and job criteria without editing code. I would deliver this in a scoped MVP: email intake, CV/link extraction, dedupe/history checks, Google Sheet/CSV/XLSX exports, shortlisted-candidate workflow emails, recruiter status tracking, and setup docs. Before we lock the full build, I can share a compact sample against 5-10 anonymized CVs so you can confirm scoring, fields, and workflow shape. Ready to get started. Best, Kaustubh

@ApieTech

Hi, read through the spec — Gmail-monitored CV ingestion, link extraction (LinkedIn / portfolio), duplicate + multi-post detection, Google Sheets storage with Excel/CSV export, AI screening with dynamic range filters, full interview workflow, and DPDPA-compliant data handling. 4-5 cycles a year, so it should sit quiet between runs. How we'd build it: - Node.js + Gmail API watch + Pub/Sub for real-time mail ingestion - PDF/DOCX parsing via pdf-parse + mammoth, OCR fallback only if needed - Candidate fingerprint on normalised email + phone + name for dedupe; multi-post tracking via job-ID tag (no overwrite) - Google Sheets as canonical store + small range-selection UI in React (Salary / Distance / Experience min-max) - LLM call (GPT-4o-mini or Claude Haiku) for screening — cost stays under a few rupees per CV at 150 CVs/week For DPDPA: service-account-scoped Sheet access, retention + purge steps in the user manual. Ping me with a sample JD and I'll align the screening rubric before we start.

@syedarimshaf

Experienced in web development, AI integration, problem solving, and creating efficient digital solutions daily.!

@mansehejsingh

Hi, I can build a lightweight AI-assisted recruitment workflow that screens incoming CVs, tracks duplicates, stores candidate records, and helps shortlist based on your job criteria. My approach would be: 1. Confirm the application sources, mailbox format, CV file types, and screening criteria. 2. Build ingestion and parsing for resumes and emails. 3. Add duplicate detection, candidate status tracking, and a simple admin view or spreadsheet-backed dashboard. 4. Add AI scoring and shortlist notes with clear reasons, not just a black-box score. 5. Test with sample CVs and hand over setup notes. I would keep the first version cost-effective and maintainable, using APIs and storage choices that match your budget and hosting preference.

@angelitstudio1

Angel IT Studio is a strong fit for this recruitment bot because the core need is not just AI scoring; it is a reliable Gmail-to-Sheets workflow with duplicate handling, candidate tracking, exports, and clear recruiter controls. I would build this as a low-maintenance Google-first system using Gmail/API or Apps Script, Google Sheets as the operating database, optional OpenAI/Gemini screening, and export paths for CSV/XLSX. The first version would focus on dependable parsing, auditability, and simple recruiter review rather than a heavy platform. Suggested delivery plan: 1. Intake Gmail monitoring, attachment capture, link extraction, and candidate normalization. 2. Duplicate/multi-post detection using email, phone, name, and historical records. 3. Sheets dashboard with configurable ranges for salary, distance, experience, qualification, and status. 4. AI screening summaries/match scores against dynamic job criteria, with human-review flags. 5. Automated acknowledgment / shortlisted / interview-status emails plus setup documentation. For DPDPA/privacy, I can implement consent-friendly retention, access controls, logging, and data purging steps based on rules you provide. I will not treat this as legal advice; I will build the technical controls and document assumptions clearly. Timeline: 28 days for a practical working system, testing, and handoff.

@mohameda093

Delivered a recruitment AI subsystem with 96.3% routing accuracy and 9.4s average end-to-end latency. In a prior project I reduced end-to-end latency by 40%, demonstrating the speed and stability the HR bot needs for email ingestion and processing. My PeakTime AI Subsystem already parses unstructured text, classifies candidates, and routes queries—skills directly applicable to screening resumes and tracking multi-post applicants. What is the expected daily volume of incoming emails and the desired response time for the bot to process and categorize each application?