Vacancy search & MVP AI Agent

@irsolutions

I understand your need for an automated agent to streamline the process of matching candidates with relevant open roles. With over a decade of experience in full-stack architecture and high-scale systems, I have successfully built and scaled complex solutions, such as serving over 1 million users and developing high-security FinTech applications. This background directly applies to the challenges of implementing an AI Agent for vacancy search and matching. My strategic insight for this project is to leverage modern NLP tools like Python and LangChain to extract and normalize candidate data efficiently. In a previous project, I successfully built and deployed a similar solution for matching user profiles with relevant content, showcasing my ability to handle the complexity of this task. I encourage you to take action and reach out to discuss how we can collaborate on developing the MVP AI Agent for your project. I am confident in my ability to deliver a working script or micro-service that meets your requirements and exceeds your expectations. Let's connect to discuss the roadmap for this innovative project. Thank you for considering my proposal.

@OlektraGroup

Hi. You need an automated agent to unify candidate data from CVs, LinkedIn, and Metaview transcripts, then map those normalized profiles against live job boards while filtering out agency noise. I recently built a similar pipeline for an automated recruitment platform, where I used LangChain and GPT-4o to extract structured competencies from unstructured meeting notes and resumes. To ensure high-quality matching, I’ll implement a RAG-based retrieval system using Qdrant for vector search, which allows us to rank vacancies by semantic fit while keeping operational costs low. I’ve previously optimized similar Python scrapers to respect platform rate limits and GDPR compliance, ensuring the agent remains robust and scalable. How are you currently handling the authentication for LinkedIn and job board access to avoid detection?

@OscarMozo

Hi, I can help you with this. I am a developer with extensive experience with automations and integrations. I've helped clients with similar projects. Let me know your interest, Sincerely, Nicolas

@kamran2012

Interesting project, I will build the AI agent as a Python micro-service — CV/LinkedIn/Metaview ingestion, unified candidate profiling, and ranked vacancy matching with fit explanations and automated outreach drafts. For the extraction layer, I will use structured output from an LLM to normalize skills and preferences across all three sources into a single schema, then apply embedding-based similarity scoring against vacancy data. This avoids brittle keyword matching and lets the ranking improve as you refine the profile fields — while keeping token costs low by chunking inputs before they hit the model. Questions: 1) For vacancy sourcing, do you have specific company career pages or job boards in mind, or should I design a pluggable scraper architecture? Looking forward to talking through the details. Kamran

@alizain721

You want a single candidate record that reliably merges CV, LinkedIn and Metaview notes and returns a ranked, explainable shortlist — I can build that. The real challenge isn’t scraping jobs — it’s normalising noisy, overlapping skill claims across sources, deduping experience, and producing a defensible match score that also excludes agency listings and surfaces hiring contacts. I recently delivered a similar pipeline for a recruitment SaaS: ingested resumes + LinkedIn, normalized skills with an ontology, indexed company career pages, and produced ranked shortlists with short rationales used by their sourcing team. I’d implement lightweight Python microservice: connectors to ingest files/LinkedIn export/Metaview JSON into a canonical candidate schema, a hybrid extractor (NER + few-shot LLM prompts) to normalize skills/experience/preferences, a RAG index of career pages / vacancy API, deterministic filters to exclude agencies and heuristics to find hiring managers, plus anonymised email templates and a manual-send script. GDPR via local processing/pseudonymisation and configurable retention. Quick question: do you have access to any internal vacancy API or should I plan primary reliance on scraping public career pages and job boards? Also, can you share a sample Metaview transcript format?

@UzeeTech

As a highly-experienced Full-Stack Developer with a specialization in AI and Machine Learning, I am particularly skilled in web scraping and data integration. I have successfully built up numerous production-grade applications, including ones that required complex crawling and data extraction like your vision of the automated Agent. My strong proficiency with Python, popular NLP frameworks such as TensorFlow, PyTorch, and OpenAI API will enable me to train robust models to accurately parse and normalize data from various sources. Given my familiarity with LinkedIn and job board terms along with my ability to strike a balance between data processing cost and performance, I can ensure that not only will your project strictly adhere to GDPR and privacy regulations across the US, UK, and Europe; but also deliver tangible business value by leveraging all available relevancy criteria for matching the candidate profiles. What differentiates me further is my integral team player approach - it's not just about delivering code, it's about enabling you with an end-to-end solution that you can easily follow up on. This reflects in my clear communication style, my clean and well-documented codes that are scalable, secure, optimized for performance, and can be expanded as-needed. Adding cherry on the top would be my 98% on-time delivery track record; reminding you again of why clients like you continuously choose me. Let's connect now and convert your vision into a fruitful reality!

@juanjosec63

Hi, I have 9 years experience in Python, NLP/LLM pipelines, web scraping, APIs, RAG, and NoSQL, with hands-on work in resume parsing, data extraction, ranking systems, and scalable automation services. For this project, I am going to build a clean Python-based pipeline that merges CVs, LinkedIn data, and Metaview notes into one candidate profile, extracts skills and preferences across all sources, matches them against real vacancies with clear fit scoring, filters out agency roles, and generates outreach-ready anonymised email drafts with GDPR-aware handling and code that is easy to extend later. You can expect clear communication, fast turnaround, and a high-quality result. Best regards, Juan

@rogelitoc

Hello, In my opinion, the problem of this project is that it requires seamless integration of multiple data sources while ensuring robust data extraction and compliance with privacy regulations. I will design a microservice architecture using Python with LangChain for NLP tasks and web scraping libraries for data ingestion. The service will link candidate records and extract relevant information from the CV, LinkedIn, and Metaview notes, normalizing skills and experience. I will implement a ranking algorithm for job matches while excluding agency listings, automating the email process to hiring managers, and ensuring compliance with GDPR and platform terms. The deliverable will include a fully functional script with a simple CLI, sample output for three candidates in JSON or CSV, and a comprehensive README for setup and future modifications. My background includes expertise in data parsing, web scraping, and compliance-focused software development. I can start immediately. Regards.

@amadorjay

Hello, The main engineering challenge lies in effectively aggregating and normalizing data from disparate sources while maintaining a coherent candidate record. Ensuring accurate extraction of skills and competencies across these varied formats adds complexity, especially when preferences must also be discerned. Additionally, the system must efficiently query multiple job boards while adhering to compliance with GDPR and LinkedIn's terms. What specific data structures do you envision for linking candidate profiles across the sources? Are there existing integrations with job boards that the agent should leverage? Furthermore, how will privacy concerns impact the data handling and storage strategies throughout this process? I look forward to discussing the architecture in more detail.

@techsty2014

Good to see this project, I will build your MVP agent to merge CV, LinkedIn, and Metaview data into one candidate profile, extract skills, experience, and role preferences, then rank live vacancies with clear fit reasoning and draft outreach messages for hiring contacts. I will keep the pipeline modular, well documented, and lightweight so setup stays fast and future expansion stays practical. I would separate ingestion, profile normalisation, vacancy retrieval, compliance filtering, and ranking into distinct services so LinkedIn and job board rules can be enforced without touching the matching logic. Questions: Will vacancy search rely mainly on public career pages and job boards, or do you also have an internal vacancy API? How should Metaview data be provided at MVP stage: raw transcript, summary, or API access? Do you want the first version as a CLI tool or a simple web UI? Ready to start whenever you are. Faizan

@Hubready

Hello! I am a US-based senior software engineer with extensive experience in AI automation and data processing. I carefully reviewed your project on creating an automated agent for CV and LinkedIn analysis, and I am excited about the opportunity to assist you in achieving your goals. With over 15 years of experience in cloud computing, web scraping, and machine learning, I have the skills and expertise needed to develop a robust MVP AI Agent. My approach emphasizes not only technical soundness but also practical implementation. Could you please clarify the following questions to help me better understand the project? 1. What specific data points from the CV and LinkedIn profile are most important for the agent to analyze? 2. Do you have any preferred machine learning frameworks or tools for this project? I have built several AI-driven applications, including a workflow automation tool for a mid-sized company and a custom API for a data analytics platform. My experience includes LLM integrations and intelligent workflow automation that could be beneficial for your project. I suggest a phased approach, starting with requirements gathering, followed by data extraction, model training, and testing. I am committed to delivering a solution that meets your needs and drives results. Looking forward to discussing this further. Best, James Zappi

@pinkeegupta

Hi, I have reviewed your project requirements and I’m confident I can deliver accurate, data-driven, and scalable solutions for your needs. I bring 9+ years of combined experience in Python development, Data Science, Data Analytics, and Business Intelligence, helping clients turn raw data into meaningful insights and actionable dashboards. My Core Expertise Includes: Node js , React Js, Mongo , Blockchain, crypto currency Python Development: Pandas, NumPy, Scikit-learn, FastAPI, Flask, Django Data Science & Machine Learning: Data cleaning, EDA, predictive modeling, AI/ML solutions Data Analytics: Statistical analysis, reporting, automation, data mining Power BI: Interactive dashboards, DAX, Power Query, data modeling, KPI reporting Databases & Big Data: SQL, NoSQL, SparkML AI & Frameworks: TensorFlow, PyTorch, Cursor, Calude, gemini, nano, chatgpt. I focus on clean code, clear insights, performance optimization, and business-oriented outcomes. I ensure timely delivery and transparent communication throughout the project lifecycle. Let’s connect to discuss your requirements in detail and define the best approach for your project. Looking forward to working with you. Regards, Anju

@larrylbrown3rd

✅ Hi - I'm Larry from Atlanta. Here, the main risks are weak profile merging across multiple sources, noisy extraction from summaries, non-compliant LinkedIn or job-board handling, and job ranking that looks plausible but is hard to trust. I solve that by using explicit candidate-linking rules, structured extraction with validation, compliant data-source boundaries, explainable ranking logic, and clear source-backed rationale in the shortlist. I recommend Python + FastAPI + PostgreSQL + LangChain/LlamaIndex because it is a strong fit for document parsing, profile consolidation, ranking pipelines, structured JSON output, and a simple local CLI or web UI MVP. For job discovery, I would use approved APIs, company career pages, and compliant job-source connectors rather than risky scraping patterns. I have worked on AI-driven workflow systems, structured data extraction pipelines, admin review flows, and API-based automation where accuracy, traceability, and maintainable backend architecture matter. I also have experience building Python services around document processing, structured outputs, and background job handling. I can start immediately and would love to discuss more about this project - how I can dive into your project right away. Thank you, Larry