AI CV Shortlisting SaaS MVP

@infowider11

⭐️⭐️⭐️ AI-Powered CV Shortlisting Web App ⭐️⭐️⭐️ Hello, I checked your JD and fully understand the requirement to deliver a production-ready, cloud-hosted platform for fast résumé shortlisting. Features: • Vacancy creation with competency rubrics, reusable templates, and weighting • Candidate intake via drag-and-drop, bulk ZIP/CSV, and PDF/Word parsing • AI matching engine generating ranked scores with semantic analysis and “why” explanations • Real-time review dashboard with filtering, searching, exporting, and favourite/reject functionality • Role-based user management with JWT/OAuth, multi-tenant separation, and secure cloud deployment Many more… Let’s chat…. Thanks

@AwaisChaudhry

Hello, I will build a cloud-hosted SaaS MVP that lets oil-and-gas recruiters post vacancies, define competencies with reusable rubrics, and upload CVs (single or bulk) for fast, accurate shortlisting. The stack is chosen to be robust and easy for another engineer to extend: Node/Typescript with React or Python/FastAPI with Vue, containerized, with a CI/CD pipeline and one-click provisioning. I’ll design a clean, multi-tenant architecture with role-based access (recruiter, manager, admin) and JWT/OAuth, and ensure real-time updates on the dashboard, exportable shortlists, and secure file processing. The approach keeps you in control of end-to-end decisions and focuses on reliability, test coverage, and predictable performance, targeting under 10 minutes from vacancy to shortlist and handling 1,000 concurrent CV parses with sub-3-second responses on moderate cloud hardware. Proposed plan includes: creating a reusable vacancy rubric with weighting; drag-and-drop and bulk CV intake with automatic document parsing; an evaluation engine that returns a score, rank, and a concise explanation per candidate; a live review dashboard with filters, search, and exports; strict RBAC and multi-tenant separation; and a containerized build with tests and automated deployment. I’ll demo past HR data-workflows and, if you want, share a sample rubric and a mini MVP storyboard. What is your top priority for the MVP—speed from vacancy to shortlist, accuracy of rankings, or ease-of-use for recrui

@OutsourceMan

As the head of CnELIndia, a premier web and app development enterprise, I offer you over 18 years of experience honed in delivering top quality projects on time and within budget. We have successfully constructed numerous web applications boasting various functionalities akin to what you seek, making us the ideal choice for building your AI CV Shortlisting SaaS MVP. In regards to your project's acceptance criteria, our expertise in technologies like Node.js, PHP, and Python align effortlessly with your tech stack flexibility. This ensures that we can provide the well-documented and extensible solution you desire for a seamless future as other teams can effortlessly maintain or extend your product without hitches. Lastly but certainly not least, our team has mastered containerized builds with automated tests, CI/CD pipelines as well as JWT/OAuth protocol for secure role-based access and multi-tenant separation. Combined with this, our proficiency in employing the cloud provider means your system will sustain a high intensity of work without sacrificing performance. Trust us with your vision; we will bring it to fruition with precision and excellence.

@shivmirthyusl

Hi, I understand you need a production-ready, cloud-hosted web app that helps oil-and-gas recruiters move from vacancy setup to ranked CV shortlists in minutes. The first release needs rubric-based vacancy creation, bulk résumé upload and parsing, AI scoring with clear “why” explanations, real-time review tools, and secure multi-tenant access with CI/CD from day one. My suggested stack would be React + FastAPI, with PostgreSQL, background workers for parsing/scoring, and Docker-based deployment with automated tests and CI/CD. A few questions: should the scoring model be fully rules-based first, or hybrid with LLM support? Do you need ATS integration in phase one? Any preferred cloud provider? Estimated timeline: 6–8 weeks. Happy to discuss further. Best, SNR

@n2rtechnologies

Hi there, I reviewed your requirements and this looks like something we can handle well. Building an AI-powered CV shortlisting MVP for oil-and-gas recruiters is definitely in our wheelhouse — we've done similar AI-integrated SaaS products before. I noticed you're looking for a cloud-hosted solution, which means we'll need to nail the infrastructure setup alongside the AI model integration. A couple of quick questions: are you leaning toward any specific cloud provider, and do you have training data ready for the AI component? I have delivered 1500+ web and mobile projects over 14+ years — happy to share relevant examples. Thanks, Hasan

@tangramua

Dear ,   We carefully studied the description of your project and we can confirm that we understand your needs and are also interested in your project. Our team has the necessary resources to start your project as soon as possible and complete it in a very short time.   We are 25 years in this business and our technical specialists have strong experience in PHP, Python, Resumes, Node.js, Web Development, Web Application, SaaS, AI Model Development and other technologies relevant to your project.   Please, review our profile https://www.freelancer.com/u/tangramua where you can find detailed information about our company, our portfolio, and the client's recent reviews.   Please contact us via Freelancer Chat to discuss your project in details.    Best regards, Sales department Tangram Canada Inc.     

@MashoodurRehman1

I am an experienced ML/AI engineer with a strong background in building scalable SaaS applications tailored for the recruitment industry. My proficiency in Python, FastAPI, and cloud deployment aligns directly with the requirements for the AI CV Shortlisting SaaS MVP. By leveraging these technologies, I can ensure a clean, robust architecture free from legacy constraints while offering swift and accurate AI-driven candidate shortlisting. In previous projects, I have successfully implemented AI matching engines and semantic processing techniques for HR systems. I am adept at creating containerized builds with automated testing setups and have extensive experience with CI/CD pipelines to streamline cloud deployments. My approach includes crafting a seamless user experience that accommodates role-based access, real-time data processing, and a scalable infrastructure capable of handling high concurrent loads efficiently. I would appreciate the opportunity to discuss your project in more detail. Could you provide insight into preferred cloud providers and any demo you might have of current prototypes? Thank you for considering my proposal.

@neerajdhameliya

Hello, Shortlisting hundreds of oil and gas CVs usually stalls on technical certs generic ATS platforms miss. I’ll build a high-concurrency SaaS that scores candidates against weighted rubrics, turning a day’s manual screening into a 10-minute automated shortlist. Solution: I propose a FastAPI (Python) + React stack. Python’s ecosystem is superior for semantic matching and parsing, while FastAPI’s async nature ensures sub-3s response times under load. We’ll use PostgreSQL with Row-Level Security for multi-tenant safety and a Celery/Redis queue to handle bulk ZIP uploads without UI lag. Question: Do you have a preferred LLM provider, like a private Azure instance, to handle the "why" explanations? Best, Niral

@extreamcode

Hello, I’ve read your AI CV Shortlisting MVP brief and I’m confident I can deliver a production-ready, cloud-hosted SaaS with end-to-end ownership of architecture. I’ve built AI-driven recruitment tools and SaaS platforms with multi-tenant security, JWT/OAuth access, and CI/CD deployments, ensuring fast, scalable parsing for 1,000 concurrent CVs and real-time dashboards. I propose a stack that balances speed and extensibility: Node.js with TypeScript for the API, React on the frontend, and a Python microservice for the AI/ranking engine, all containerized and orchestrated for seamless deployment. Data ingestion will support drag-and-drop, ZIP/CSV uploads, and robust document parsing (PDF/Word). The vacancy workflow will allow creating positions, defining competencies with weighted rubrics, and saving rubrics for reuse. The AI matching will combine rubric-driven scores with semantic similarity to generate per-candidate explanations. The review dashboard will support filtering, search, export to Excel/CSV, and real-time status updates. I’ll also establish a clean CI/CD pipeline with automated tests and one-click provisioning on your chosen cloud provider. I’ve shared an initial estimate based on your description, and once we go over a few technical or functional details, I’ll confirm the exact cost and delivery schedule. 1) Do you have a preferred cloud provider and a target region for multi-tenant deployment and one-click provisioning? 2) Are there any industry-specific

@ZohaibRoy

⭐⭐⭐⭐⭐ Create a Cloud-Hosted Recruitment App for Oil and Gas Recruiters ❇️ Hi My Friend, I hope you're doing well. I've reviewed your project requirements and see you're looking for a cloud-hosted recruitment application. Look no further; Zohaib is here to help you! My team has successfully completed over 50 similar projects for web applications. I will build a user-friendly platform for recruiters to shortlist résumés quickly and efficiently. ➡️ Why Me? I can easily create your recruitment app as I have 5 years of experience in web development, focusing on user management, data parsing, and AI integration. My expertise includes building scalable applications and implementing real-time updates. I also have a strong grip on modern frameworks, ensuring a robust and efficient solution. ➡️ Let's have a quick chat to discuss your project in detail. I would love to show you samples of my previous work, showcasing my skills in building efficient web applications. Looking forward to chatting with you! ➡️ Skills & Experience: ✅ Web Development ✅ User Management ✅ Data Parsing ✅ AI Integration ✅ Real-time Updates ✅ Cloud Deployment ✅ CI/CD Pipeline ✅ Database Design ✅ API Development ✅ JavaScript Frameworks ✅ Version Control ✅ Automated Testing Waiting for your response! Best Regards, Zohaib

@diektech

Hi, This is Elias from Miami. I checked your project description and understand you want to launch a cloud-hosted web app that helps oil-and-gas recruiters upload large volumes of CVs, score them against reusable vacancy rubrics, and produce ranked shortlists with clear AI-backed explanations. The key is making it production-ready from day one, with strong parsing, real-time review workflows, multi-tenant access control, and a backend that can scale cleanly. I’ve worked on AI-driven workflow platforms, multi-user dashboards, and document-processing systems where performance, explainability, and clean architecture were all critical. My approach would be to build this as a modular web platform with a strong parsing pipeline, rubric + semantic matching engine, real-time review dashboard, and secure multi-tenant backend, then containerize everything with CI/CD so the system is easy to deploy and extend. Q1 – Do you want the first release to support English CVs only, or should multilingual parsing and matching be considered from the start? Q2 – For the AI scoring, do you already have sample rubrics and historical hiring decisions that can help calibrate the weighting and “why” explanations? Q3 – Which cloud provider are you leaning toward for phase 1, and do you want the architecture optimized first for speed of launch or for higher concurrent parsing volume from day one? Looking forward to hearing from you.

@elvirainfotechpl

Hi, How do you envision balancing speed and accuracy in your AI CV shortlisting engine, especially given the high concurrency and real-time requirements? I would approach this project by designing a clean, scalable microservices architecture using Node.js with Typescript and React for the frontend to ensure maintainability and extensibility. The AI matching engine would be modular, leveraging NLP models trained or fine-tuned on recruitment data for semantic matching. Containerized deployment with CI/CD ensures reliable, easy cloud provisioning. Role-based access and multi-tenant design will secure user data. This structured yet flexible plan aims to deliver a highly performant and user-friendly MVP by launch day. Please share any additional details or preferences you have. ---

@Fazeennazar

Hello Greetings, After reviewing your project description, I am confident and excited to work on this project for you. a, I have some crucial points and questions to clarify. Please leave a message in the chat to discuss this, and I can share my recent work that is similar to your requirements. Thanks for your time! I am excited to hear from you soon. Best regards

@SBITServices

Hi, I will develop a robust AI CV shortlisting SaaS MVP that streamlines oil-and-gas recruitment processes. The solution will include vacancy workflow creation, candidate intake automation, AI matching engine for scoring and ranking, user-friendly review dashboard, and secure user management. I will ensure seamless deployment with containerization, automated testing, and CI/CD pipeline integration. Let's discuss further details in chat. Regards, Sai Bhaskar

@toni5705

Hello, I can build your high-performance résumé shortlisting platform from the ground up. I specialize in architecting AI matching engines that transform unstructured PDF/Word data into actionable, weighted scores, ensuring sub-3s response times even under heavy load. My Technical Strategy: AI Matching Engine: Implementing a semantic ranking layer using Python/FastAPI and LangChain. I will design custom weighting logic that maps competencies directly to rubrics with explainable AI outputs. Scalable Intake: Using Celery/Redis for asynchronous bulk processing (ZIP/CSV), ensuring the system sustains 1,000+ concurrent parses without latency. Modern Frontend: Developing a high-fidelity React/TypeScript dashboard with real-time updates, advanced filtering, and one-click exports. Security & Multi-tenancy: Implementing strict database-level separation and RBAC via JWT/OAuth2 to protect sensitive recruiter data. Cloud-Native Deployment: Fully containerized (Docker) architecture with a robust CI/CD pipeline and automated testing. I have delivered similar AI-driven HR solutions; I would be happy to share specific demos and technical details via private message. Best regards, Anthony Muñoz

@LiveExperts

Hello, As a seasoned member of a cohesive team at Live Experts, I've gained in-depth knowledge and experience in several areas relevant to your project. At its core, your project demands precision and efficiency through AI automation in parsing CVs and integrating them into a systematic workflow. As expected, this includes architecture, role-based access using JWT/OAuth & multitenant separation, building tests, CI/CD pipeline deployment and more; all of which my team is highly competent at. And yes, I've done HR projects with AI-driven functionalities that are fully operational today - highly apt for profiling candidates vis-a-vis validated competency standards. Moreover, our ML/AL capabilities ensure seamless bulk upload compatibility as you desire. We understand the time-sensitive nature of the business world and will make sure that your software project is running smoothly within the stipulated timeframe. We propose a tech stack of Python (FastAPI) with Vue.js on the frontend. The templating engine allows us to create modern, engaging elements for an intuitive user interface while maintaining speed with FastAPI backend. All of this will be backed up by well-documented code as per best practices for easy maintainability. Your proposed acceptance criteria see a 1k concurrent CV parser count within 3 seconds response time. With our sound understanding and use of technology like TensorFlow, Keras & OpenCV for efficient computing po Thanks!

@AITSoft

Hello, I understand you want a cloud-based web app for oil-and-gas recruiters to quickly shortlist CVs. The app will handle vacancy creation with detailed scoring rubrics, bulk candidate uploads with auto PDF/Word parsing, and a scoring engine that ranks candidates and explains scores. It will have a real-time dashboard for filtering and exporting results and secure role-based access with tenant separation. I’ll build an app that meets your 10-minute flow goal, scaling to handle 1,000 concurrent CV parses with quick responses. I suggest using Node.js with Typescript and React for a scalable, maintainable solution, but I’m open to other tech you prefer. I’ll focus on clean architecture, thorough testing, and smooth deployment with containerized builds and CI/CD. I have done AI-driven HR tools before and can share demos. What cloud provider do you prefer for deployment? What format and fields do your rubric competencies have? How do you currently handle candidate data privacy and compliance? Do you have preferred libraries or frameworks for AI and PDF parsing? What level of documentation detail do you expect for extending the codebase? What cloud provider would you like to use for hosting and deploying the application? Thanks,

@DeveloperStation

Hi there, We’ve built AI-driven platforms that streamline candidate selection for recruiters, including a product that uses AI to match candidates with job descriptions. We understand the importance of delivering a solution that truly meets user needs. For your project, we can leverage our expertise in AI and web development to create a robust, production-ready MVP. We’ll ensure that the foundational elements are strong, so you can confidently add new features later without worrying about technical debt. Let’s schedule a 10-minute call to discuss your project in more detail and see if I’m the right fit for your needs. I’m eager to learn more about your exciting project. Best, Adil

@AhteshamAK

Hello, and thank you for sharing such a clear vision for your AI CV Shortlisting SaaS MVP. As Ahtesham, I can help you move from concept to a robust, cloud-hosted product that oil-and-gas recruiters can trust from day one. I will deliver a clean, extensible architecture with end‑to‑end ownership, no legacy constraints. I propose a Python/FastAPI backend for high‑performance parsing and AI scoring, paired with a React dashboard for a smooth, real‑time workflow. The system will support multi‑tenant access, JWT/OAuth security, and high‑throughput CV parsing with semantic‑matching models tailored for technical roles. CI/CD, containerisation, and automated testing will ensure reliability and rapid iteration. Your acceptance criteria align perfectly with my approach, particularly the under‑10‑minute flow, rubric‑aligned scoring explanations, and scalable batch processing. I’ve previously built AI‑driven recruitment and document‑classification platforms, and I can demo similar scoring pipelines. Best regards, Ahtesham