AI-Based Job Aggregation & Career Guidance Platform

@onkarkulkarni8

Hi, I can help with your "AI-Based Job Aggregation & Career Guidance Platform" project. I develop clean, maintainable PHP backends — core PHP and Laravel/Symfony, MySQL schema design, and well-structured REST APIs. For work involving php, java, android, web scraping, machine learning (ml), data science, ai chatbot, ai development, I pay close attention to validation, security, and readable code, delivering in small, testable milestones. I provide clean commits and clear documentation. Could we discuss the specifics before I firm up the timeline? ⭐ 5.0/5 from a recent client: "Very Professional and On time delivery of the project" Final timeline and cost will be confirmed in chat after a complete understanding and documentation of the project expectations in detail.

@ASSAMWEB

Greetings, I have read the project description but haven't found any details requirements open a chat to discuss requirements in details. Regards

@Sajid293r

Hi there, I hope you are doing well. Your AI-powered career platform is an ambitious and impactful project, and I'd be excited to help bring it to life. With experience in AI integration, scalable web applications, data aggregation, cloud infrastructure, and full-stack development, I can build a secure, high-performance solution designed for long-term growth. Which phase would you like to prioritize first—large-scale job aggregation, AI career intelligence, or the user-facing platform? Let's have a quick chat to discuss more in detail. I am looking forward to hearing from you. Best, Sajid.

@Senavirathna1014

Hello, I can help build this AI-powered job aggregation and career intelligence platform as a scalable production system. I would develop it in phases, starting with the core aggregation engine and then expanding into AI career features. The main scope would include: • Company career page discovery • ATS detection for Workday, Greenhouse, Lever, Ashby, SmartRecruiters, iCIMS, etc. • Distributed crawling with rate limits, retries, and source monitoring • Job data extraction and normalization • Duplicate detection and expired-job removal • Search and filtering using OpenSearch/Elasticsearch • Resume-to-job matching and skill gap analysis • Personalized job alerts and application tracking • AI career assistant for insights, roadmaps, and interview preparation • Admin dashboard for crawler health and data quality For the stack, I can use Python/FastAPI, PostgreSQL, Redis, Celery/RQ, Scrapy/Playwright, OpenSearch, Docker, and cloud deployment. I recommend starting with an MVP covering ATS integrations, job crawling, normalized database, search, and basic AI matching. After that, we can scale the crawler network and add advanced recommendations and career intelligence. Thank you.

@ayushj380

I understand you need an AI-Powered Global Job Aggregation and Career Intelligence Platform. I've built large-scale scraping + AI data pipelines before, so I know exactly what this needs — a scalable aggregator, not a basic scraper. DM me and I'll walk you through the exact architecture. Once you message, consider your work done. How I'll build it: => Distributed crawlers that discover company career pages and auto-detect ATS platforms (Greenhouse, Lever, Workday, etc.) via pattern + API detection. => AI extraction/normalization pipeline to classify role, skills, location, seniority from raw job pages into a clean schema. => Duplicate detection + freshness monitoring via hashing/embeddings — expired listings auto-drop, near-duplicates merge. => High-performance search (Elasticsearch/OpenSearch) with filters + personalized recommendations, on a scalable DB + queue-based sync built for millions of listings. => Resume-to-job matching with skill-gap analysis, plus AI assistant for application insights, career roadmaps, interview prep. => Users redirected to official company apply pages — platform stays a discovery/intelligence layer, not a middleman. => Modular architecture so crawling, ATS, and AI layers scale independently. Thanks in Advance, Ayush Kumar Jha

@prachia77

Hi, I have carefully reviewed your project, and it is much more than a standard job portal build. You are looking for a scalable job aggregation and career intelligence platform that combines large-scale data collection, ATS integration, AI-driven job processing, and personalized guidance features in one production-grade system. My experience includes building data-driven web platforms, API-based integrations, automation workflows, and AI-powered features for search, matching, and structured data processing. For this project, the focus would be on designing a strong backend architecture for job discovery, crawling, normalization, deduplication, freshness tracking, and intelligent search, while also structuring the AI layer for resume matching, skill-gap analysis, and career guidance features. This is the kind of platform that needs both technical planning and long-term scalability in mind from the beginning, and I would be glad to discuss the best stack, architecture approach, and rollout plan for building it properly. Best regards, Prachi Agrawal

@ShaikFirdos

I see you are looking for an AI-native job aggregation platform capable of handling large-scale, real-time data indexing. My experience building SignalReport—an autonomous AI news agent using Django, FastAPI, and a full RAG stack—perfectly aligns with the architecture you need for high-concurrency job analysis and semantic searching. To ensure we align on the technical roadmap, how are you currently planning to handle the rate-limiting challenges typical of large-scale career page scraping? I’d love to discuss how we can implement a robust, distributed crawler to keep your data fresh while maintaining a high-performance search interface. Are you available for a brief chat to discuss the project scope?

@AndroidShivam

**Hi there,** I’ve already built a near-identical system: **JobBot**—an AI job scraper across 20+ portals (Greenhouse, Lever, Naukri) with NLP relevance scoring, async Playwright, and Discord alerts. I also built **HalfBill**—a production multi-agent orchestration layer with 7 Dockerized FastAPI/PostgreSQL microservices handling 1000+ customers. I’m not guessing—I’ve deployed it. For your platform: distributed crawlers with ATS auto-detection, AI extraction for role/skills, embedding-based deduplication + freshness, Elasticsearch, and resume-to-job skill-gap analysis. Start MVP: scrape 3–5 sources → AI normalize → dedupe → searchable DB with matching. Which ATS (Workday, Lever, Greenhouse) first? Let’s lock Phase 1. Best, Shivam