Backend Engineer for RAG in Healthcare Setting

@WebChapters

Hello, I hope you're doing well. I'm excited about the opportunity to design and implement your production-grade RAG backend for a controlled knowledge system in a clinical context. Ensuring accuracy, traceability, and rule enforcement is critical, and I am well-equipped to deliver on these requirements. To better understand your needs, could you share more about your preferred timeline for this project? Are there specific features within the RAG architecture that you consider top priority? Lastly, do you have any particular branding guidelines or preferences for the API layer and documentation style? I look forward to crafting a robust system that aligns with your objectives and maintains the integrity of your clinical data. Best, Azeem Amin

@kamran2012

Hi there, I will build your production-grade RAG backend with a strict retrieval-first architecture: ingestion pipeline from WordPress content, semantic chunking with metadata tagging, vector database setup, a validation layer that enforces your safety rules, and an API layer for query handling - all deployed on your Hetzner infrastructure. For the "no-retrieval = no-answer" rule, I will enforce this in the orchestration layer before the LLM is ever invoked. After the vector search returns results, a similarity threshold gate checks relevance scores. If no chunk passes the threshold, the pipeline short-circuits and returns a controlled fallback response - the LLM never sees the query. This keeps enforcement deterministic and outside the model, which is critical in a healthcare context where you cannot rely on prompt instructions alone to prevent hallucination. For context separation, I will attach metadata (topic, treatment type, document ID, version hash) to every chunk at ingestion time and apply pre-retrieval filters so the vector search only hits the correct partition. This also gives you traceability back to source documents and content versions when WordPress content is updated and re-ingested. Thanks and best regards, Kamran

@ZohaibRoy

⭐⭐⭐⭐⭐ Build a Reliable RAG Backend for Your Knowledge System ❇️ Hi My Friend, I hope you're doing well. I reviewed your project needs and see you're looking for a Backend Engineer for RAG architecture. Look no further; Zohaib is here to help you! My team has completed 50+ similar projects in backend development. I will create a robust RAG backend that ensures safe and accurate responses from your structured knowledge base. To achieve this, I will focus on strict retrieval rules and evidence-based responses. My approach ensures your system meets the clinical context's accuracy and traceability requirements, all while staying within budget. ➡️ Why Me? I can easily build your RAG backend as I have 5 years of experience in backend development, specializing in Python, vector databases, and system design. I also have a strong grip on LLM orchestration and cloud deployment, which will be crucial for your project. ➡️ Let's have a quick chat to discuss your project in detail and let me show you samples of my previous work. Looking forward to discussing this with you in chat. ➡️ Skills & Experience: ✅ Python Development ✅ RAG System Design ✅ Vector Databases ✅ API Development ✅ Cloud Deployment ✅ Data Ingestion ✅ Semantic Chunking ✅ Evidence-based Logic ✅ Traceability Implementation ✅ System Architecture ✅ Automation Infrastructure ✅ Documentation Waiting for your response! Best Regards, Zohaib

@karimmughal1

Hello, I’m interested in building your production-grade RAG backend. I have strong experience in backend architecture, APIs, data pipelines, and WordPress integrations, and I can design a reliable system where WordPress remains the content source while the AI backend runs independently on Hetzner. My approach would include building a Python-based RAG pipeline with structured ingestion, semantic chunking, embeddings generation, and a vector database (Qdrant or Weaviate) optimized for controlled retrieval. The backend will include a retrieval validation layer, metadata filtering, and API endpoints to ensure strict rule enforcement before any LLM response is generated. No-retrieval = No-answer rule implementation: The orchestration layer will enforce this rule before calling the LLM. When a query arrives, the system performs vector search with similarity thresholds and metadata filters. If retrieved chunks do not meet the minimum relevance score or context constraints, the pipeline immediately returns a controlled fallback response (e.g., “No verified information available”) and the LLM call is skipped entirely. This guarantees that responses are always evidence-grounded and traceable. The system will also support document IDs, chunk references, and version tracking to maintain traceability when WordPress content updates. I can deliver architecture design, ingestion pipeline, vector DB setup, retrieval logic, API layer, deployment guide, and documentation.

@khurshid040462

Hello! I’m excited about your project to create a production-grade RAG backend in the healthcare setting. With over 5 years of experience in developing RAG systems, particularly in safety-critical environments, I understand the importance of strict rule enforcement and accuracy you’ll need for the language model. To tackle this, I would develop a robust ingestion pipeline coupled with a reliable vector database setup, ensuring that your no-retrieval = no-answer rule is strictly adhered to. This would involve implementing API validations and metadata filtering to maintain context separation. Based on my experience building similar RAG systems, like one for a clinical research application, I can assure efficient deployment and documentation tailored for your infrastructure. A detailed timeline would be roughly 4-6 weeks for this setup. Best regards,

@kareemmaize

Hi there To build a production-grade RAG backend where an LLM queries a curated knowledge base under strict safety rules, the most important part is designing a retrieval layer that enforces evidence validation before the model is ever invoked. Since your architecture separates WordPress as the content origin and a dedicated backend on Hetzner, the system can be structured around an ingestion pipeline that extracts controlled content, performs semantic chunking, and stores embeddings in a vector database with strict metadata tagging. The orchestration layer then becomes the core safety mechanism. Each query first passes through retrieval filters and validation checks to ensure relevant source chunks exist and belong to the correct context. Only after those checks pass should the LLM be invoked to generate an answer grounded in the retrieved evidence. This approach allows responses to remain fully traceable to source documents, prevents unsupported inference, and ensures the system behaves predictably in a clinical environment. After reviewing the knowledge sources and ingestion workflow, I will provide a precise architecture plan along with a clear timeline and development estimate based on the final scope. If this direction aligns with your goals, let's discuss the details further in private chat.

@mohjib092

Hello, I have experience building RAG-based AI systems with Python, vector databases, and LLM orchestration, and I can design a secure, production-ready backend for your controlled knowledge system. For the “no-retrieval = no-answer” rule, I would enforce it in the orchestration layer: the retrieval module must return validated source chunks above a similarity threshold; if none are found, the API stops the pipeline and returns a controlled response without invoking the LLM. This ensures all outputs remain strictly evidence-based and traceable to source documents. I can handle the RAG architecture, ingestion pipeline, vector database setup, retrieval validation logic, API layer, and deployment on Hetzner, with full documentation for maintainability. Best regards.

@koolvishruth

Hi, I am Backend AI engineer with 8 years of experience and also worked on similar heath based startup on developing backend using the context given.I have worked with 116+ clients here. Let’s connect

@waqaswebmaster

Hi There, I am an experienced backend engineer with expertise in building RAG (Retrieval-Augmented Generation) systems and working with vector databases. I have hands-on experience with Python backend development, API integration, and LLM orchestration. I can design a production-grade RAG pipeline that enforces strict retrieval rules, ensuring no answer is generated without valid evidence from the knowledge base. For the "no-retrieval = no-answer" rule, I would implement a mechanism within the retrieval layer that verifies whether relevant evidence is found before allowing the LLM to generate a response. If no valid data is retrieved, the system will return a controlled fallback message to avoid generating unsupported answers. I am well-versed with the required technologies and can deliver the complete backend architecture, ingestion pipeline, vector database configuration, and retrieval logic as described. Best Regards Waqas Ahmad

@technologiesit

With more than a decade of experience as a Certified WordPress Developer, I am confident in my ability to tackle the challenges posed by your RAG backend project in the healthcare field. My team and I have a breadth of skills encompassing all stages of web development - from API Development to Backend Development - and we are exceptionally proficient with Python, the key language you are looking for in this endeavour. This, partnered with our familiarity with vector databases, puts us in an ideal position to craft and execute an effective backend architecture for your project. Given our pertinent domain expertise and technical finesse for backend development, I believe my team at Prajapati Technologies can impeccably build the RAG architecture you envision. The budget will be approached mindfully depending on the outlined scope provided we operate without any compromise on quality. Thanks for your time and consideration; I’m excited about this opportunity!

@akgarg007

Hello, Your project requires a production-grade RAG architecture with strict retrieval governance, and I have experience building Python-based LLM pipelines, vector search systems, and API-driven backends. I can design a clean RAG stack separated from the WordPress site, where WordPress acts only as the knowledge source while the AI backend handles ingestion, retrieval, and orchestration. Proposed Stack • Python (FastAPI) backend • Vector DB: Qdrant or Weaviate • Embeddings via OpenAI or open-source models • Hosted on Hetzner infrastructure • Controlled ingestion pipeline for WP exports, PDFs, or structured feeds No-retrieval = No-answer Implementation The rule should be enforced in the retrieval orchestration layer, not inside the LLM: Query → embedding → vector search Apply similarity threshold + metadata filters If no chunk passes threshold, stop the pipeline API returns controlled response such as: “No verified information found in the knowledge base.” Only when validated chunks exist → pass them as context to the LLM. This guarantees evidence-based responses and prevents hallucination. Key Deliverables • RAG backend architecture • ingestion + semantic chunking pipeline • vector database configuration • retrieval validation layer • API service for queries • deployment guide + documentation Estimated timeline: 2–3 weeks depending on ingestion complexity. I’d be happy to discuss the architecture and begin immediately. Best regards, Ashwani

@johnallenvillan1

Hello, I see you need a RAG backend that enforces strict safety constraints, especially the no-retrieval = no-answer rule and separation from your WordPress content source. I’ve built clinical-facing RAG systems before, including a medication‑guidance tool where I implemented retrieval gating and source‑traceable responses that passed internal compliance review. A core challenge here is ensuring the orchestration layer, not the LLM, controls truth boundaries. Junior developers often rely on model prompts, but the real risk lies in silent retrieval failures. The system must detect empty or low‑confidence retrieval sets and stop the pipeline before generation. I’ll design the ingestion workflow, chunking logic, and semantic filters, configure a vector database like Qdrant on Hetzner, and implement a retrieval validator that checks evidence count, metadata constraints, and context boundaries before any model call. I’ll also build a clean API layer and document the deployment process. To keep the system safe, I typically log retrieval metadata and enforce versioned document references. I can begin as soon as I receive access details. Best regards, John allen.

@GameOfWords

Backend Engineer for RAG in Healthcare Setting I’m a full-stack software engineer with expertise in React, Node.js, Python, and cloud architectures, delivering scalable web and mobile applications that are secure, performant, and visually refined. I also specialize in AI integrations, chatbots, and workflow automations using OpenAI, LangChain, Pinecone, n8n, and Zapier, helping businesses build intelligent, future-ready solutions. I focus on creating clean, maintainable code that bridges backend logic with elegant frontend experiences. I’d love to help bring your project to life with a solution that works beautifully and thinks smartly. To review my samples and achievements, please visit:https://www.freelancer.com/u/GameOfWords Let’s bring your vision to life—connect with me today, and I’ll deliver a solution that works flawlessly and exceeds expectations.

@usmanbashir12100

Hi, I have experience building structured RAG architectures and can develop a production-grade backend that enforces strict retrieval rules, traceability, and evidence-based responses for your clinical knowledge system. I will design a secure pipeline including controlled ingestion from WordPress, semantic chunking, embeddings, vector database setup, and a retrieval-validation layer that governs when the LLM is allowed to respond. No-retrieval = No-answer approach: the orchestration layer first performs vector search with similarity thresholds and metadata filters. If retrieval returns no validated chunks, the pipeline blocks the LLM call and returns a controlled response. Only verified chunks are passed as context to the model, ensuring answers remain grounded and traceable to document IDs and source versions. I can also handle API design, Hetzner deployment, and full documentation. Happy to discuss the architecture and implementation details.

@Azursol

Hello,! I’m excited about the opportunity to help with your project. Based on your requirements, I believe my expertise in PHP, WordPress, Python, HTML, MySQL aligns perfectly with your needs. How I Will Build It: I will approach your project with a structured, goal-oriented method. Using my experience in PHP, Python, WordPress, MySQL, HTML, Data Science, Backend Development, API Development, I’ll deliver a solution that not only meets your expectations but is also scalable, efficient, and cleanly coded. I ensure seamless integration, full responsiveness, and a strong focus on performance and user experience. Why Choose Me: - 10 years of experience delivering high-quality web and software projects - Deep understanding of PHP, WordPress, Python, HTML, MySQL and related technologies - Strong communication and collaboration skills - A proven track record — check out my freelancer portfolio. - I’m available for a call to discuss your project in more detail - Committed to delivering results on time, every time Availability: I can start immediately and complete this task within the expected timeframe. Looking forward to working with you! Best regards, Ali Zahid Spain

@Eliteinno

Hi there, Developing a production grade RAG system for healthcare requires a strict orchestration layer to prevent hallucinations and ensure clinical safety. Many generic builds fail because they rely too heavily on the LLM to police itself rather than using a deterministic validation gate. We will implement the strict no retrieval rule by setting a minimum cosine similarity threshold within the vector database query and using a conditional logic gate in the Python backend to terminate the process before the LLM is even invoked if no high confidence chunks are returned. Here are our questions: 1. Which specific vector database do you prefer for your Hetzner deployment to ensure optimal metadata filtering? 2. Will the WordPress content ingestion be handled via a REST API webhook or a scheduled cron job for versioning? Our team has extensive experience building high stakes RAG architectures and secure backend systems for regulated industries. We are experts in Python and vector database optimization ensuring every response is traceable and grounded. We provide 30 days of free support after project completion to guarantee your system remains robust. Feel free to check our portfolio or ask for clinical project samples in chat. Let’s discuss your project today!

@dataspro

Nice to meet you , My name is Anthony Muñoz, I express my interest in working on your project after carefully reading the requirements and concluding that they match my area of knowledge and skills. I am currently the lead engineer for the IT agency DSPro and I have more than 10 years of experience in the field. I have successfully completed a large number of similar jobs and I consider your project to be a challenge in which I would like to work and be able to make it a reality. Please feel free to contact me, it will be my pleasure to help you. I greatly appreciate the time provided and I remain attentive to any questions or concerns. Greetings

@webmasters486

Hi there, I’ve carefully reviewed the requirements for your GenAI project and I’m confident that my expertise in building NLP pipelines using Hugging Face and LangChain can meet your expectations. My experience includes working with large language models (LLMs) for Retrieval-Augmented Generation (RAG), as well as fine-tuning models with custom datasets to enhance text generation. I’ve successfully completed similar projects where I applied these techniques in Python to build robust, client-specific solutions. I would love the opportunity to discuss how I can leverage my skills to develop a tailored solution for your project. Feel free to take a look at my portfolio to get a sense of the work I’ve done: Portfolio: https://www.freelancer.com/u/webmasters486 Looking forward to hearing from you! Best regards, Muhammad Adil

@FaizalShaik80

Hello, I am a seasoned developer with over 6 years of expertise in PHP, Python, WordPress, HTML, and MySQL. I have a strong background in designing and implementing complex backend systems. I have thoroughly reviewed your project requirements for building a production-grade RAG backend for a controlled knowledge system in a healthcare setting. I understand the critical need for accuracy, traceability, and rule enforcement in this context. My proposed solution involves developing a robust RAG pipeline that enforces strict retrieval rules, ensures evidence-based responses, maintains context separation, and provides traceability. I would love to discuss your project further in chat to delve into more details and understand your specific needs. Thanks.