Senior LLM / RAG Engineer for Insurance Distribution Layer (Long‑Term)

@CANWS

Hi, I have experience building LLM-based document systems that structure complex documents and expose them through APIs. I’ve worked with retrieval pipelines using OpenAI models, LangChain, and indexed search with Elasticsearch. ✅My Approach: ingestion of policies and claims → ontology mapping for coverages/exclusions → RAG retrieval with citations → REST APIs documented via OpenAPI. Typical MVP timeline is around 6–8 weeks. Happy to review your data sources and discuss next steps.

@ASirbescu

I have successfully developed similar projects integrating RAG, parsing complex documents whilst ensuring compliance with regulations and seamless API functionality. One example of my work is 'InsuranceDocs-Automator' which used LLMs/RAG for structuring insurance documents, applying ontologies, and enabling clean APIs - fitting perfectly into the scope of your project. If given an opportunity, my agile yet robust approach will bid a viable MVP timeline based on your customized requirements.

@sabaehsan

Greetings, I’m excited about the opportunity to help you build an insurance distribution layer using LLMs and retrieval-augmented generation (RAG). The focus on structuring complex insurance documents and creating clean APIs is crucial for improving efficiency and transparency in the insurance process. I would approach this by first designing a robust data ingestion pipeline that effectively handles policies and claims, followed by establishing an ontology mapping that normalizes and categorizes the data for easy retrieval. My experience includes developing NLP solutions for long documents, particularly in regulated environments, and I have crafted APIs that ensure compliance and auditability. I aim to minimize hallucinations in model outputs by implementing rigorous validation checks and ensuring proper citation of answers. I look forward to collaborating on this project and bringing your vision to life. Best regards, Saba Ehsan

@arzoof1

Hello, I design production-grade LLM + RAG systems for complex, regulated data environments, and your insurance distribution layer is exactly the type of structured intelligence problem I specialize in—turning messy policy documents into queryable, explainable, API-first systems. I have built long-document NLP pipelines with chunking + hybrid retrieval (BM25 + vector), ontology normalization layers, citation-backed QA with audit trails, and OpenAPI-documented services with strict auth/versioning; for your MVP, I would propose: ingestion (policy/endorsement parsers + structured extraction) → coverage ontology + exclusions mapping → hybrid RAG with source attribution + confidence scoring → explainable response layer → secured REST APIs with logging for compliance. Hallucination control would rely on retrieval gating, citation enforcement, structured prompts, and answer rejection when confidence thresholds fail. An MVP would take ~10–14 weeks with a small team (1 LLM/NLP engineer, 1 backend/API engineer, 1 part-time domain consultant). Best regards, Arzoo Farooq

@LakshkarPVTLtd

With a keen eye for detail and an extensive background in data structuring, I, Neelam, believe I am the perfect candidate for your insurance distribution layer powered by LLMs and RAG project. My experience spans over six years with a wide range of satisfied clients, and over 300 completed SEO projects. As an NLP expert, I have worked on complex document parsing in a number of fields including legal and financial. I assure you that my skills in constructing normalization layers and designing ontology mappings will be put to their best use in developing interfaces to harmonize your insurance documents. My understanding of API development is another aspect that sets me apart. From building RESTful APIs to ensuring compatibility with different versions, I've got you covered. Regulatory compliance is of utmost importance when it comes to insurance distribution, and my history of creating audit-friendly processes will ensure the safety, integrity, and traceability of model outputs.

@Tacticssoft

Hi, I can help you design and deliver a production-grade insurance distribution layer powered by LLMs and a robust RAG architecture, focused on accuracy, traceability, and clean API exposure. I’ll build scalable ingestion pipelines for policies, endorsements, quotes, and claims, followed by an ontology-driven normalization layer for coverages, exclusions, and limits. The RAG stack will combine structured retrieval with citation-backed responses to minimize hallucinations and ensure explainability in regulated workflows. All services will be exposed via secure, versioned REST endpoints documented with OpenAPI, including audit logs and model output traceability. Architecture (MVP): Ingestion (parsing + chunking) → Ontology/Normalization layer → Exclusion & rule engine → Vector + hybrid retrieval → LLM reasoning → API gateway. I typically estimate 8–12 weeks for an MVP with a senior AI engineer, backend/API engineer, and part-time domain SME. I’ll also implement guardrails, confidence scoring, and source-linked responses to meet compliance and audit requirements.

@xuezhe315

Hello, As a highly proficient software engineer and developer, I bring not only knowledge and experience in building long-term solutions, but a proven track record of successful project delivery. I've worked across various domains involving regulated environments, ensuring auditability and traceability of model outputs — an essential aspect of your project. My familiarity with insurance, legal, and financial documents combined with my expertise in LLMs, RAG architecture, NLP-based document structuring, and API engineering make me an ideal fit for this role. My approach towards conducting ingestion, ontology mapping, exclusions retrieval, API documentation aligns with industry best practices and standards such as OpenAPI in consideration of your requirements. In terms of reducing hallucinations and ensuring accurate answers from the model, I will institute a robust QA process within the RAG architecture along with comprehensive answer citations to deliver precise results. Regarding estimated timeline and team composition for the MVP development phase, I advocate a collaborative approach, befitting not just the technical work involved but also taking into account our understanding of agile planning. Let's connect further addressing how my skills can drive measurable growth for your environment-sensitive insurance distribution layer!

@devbaloni1983

Hello, Hope you're doing great! I am a PHP Developer who builds secure, fast, and business-focused web applications. I work with both custom PHP and frameworks, and always ensure that every project is optimized, scalable, and easy to maintain. What I Do 1. Custom web applications & business automation tools 2. API development and integration 3. Secure login, admin panels, and dashboard systems 4. High-speed, mobile-friendly websites 5. Migration, bug fixing, and performance upgrades Why Clients Prefer My Work 1. Clean folder structure & scalable architecture 2. Fully optimized and secure coding practices 3. Excellent communication & professional approac 4. Quick turnaround time with regular updates Ready to Start Share your requirements or preferred reference — I’ll analyze it and provide: 1. Best technical plan 2. Exact timeline 3. Budget estimate Looking forward to building something amazing for you!

@AhmadXpert2410

Hi there, I can help you build a production-grade insurance distribution layer using robust RAG and well-structured APIs. As a Data Scientist working on telecom AI systems, I’ve built production RAG chatbots with custom vector databases, CRM integrations, and multi-tenant architectures—handling long policy-like documents, audit logging, and explainable outputs. I’ll design ingestion pipelines for policies/claims, normalization with ontology mapping (coverages, exclusions, limits), and a scalable retrieval layer that ensures accurate, citation-backed answers. My proposed architecture: document ingestion → parsing & entity extraction → ontology mapping layer → embedding + hybrid retrieval → guardrails (prompt constraints, validation rules, citation scoring) → OpenAPI-documented REST services with auth, versioning, and audit logs. To reduce hallucinations, I use hybrid search, confidence scoring, structured prompts, policy-rule validation, and mandatory citation tracing. This mirrors the RAG chatbot I deployed in production with FastAPI + vector DB + CI/CD automation. For an MVP, I estimate 4–6 weeks with myself as lead AI/API engineer plus optional frontend support if needed. Deliverables include ingestion pipelines, ontology schema, RAG service, documented APIs, and compliance-ready logging. I focus on clean architecture, explainability, and maintainability—happy to discuss details and tailor the roadmap to your insurance domain needs. Regards, Ahmad

@ayesha86664

Hi there! Working with complex insurance documents requires a system that’s accurate, auditable, and explainable. Without a strong LLM + RAG setup, answers can be inconsistent, incomplete, or non-compliant. I have hands-on experience building LLM-powered retrieval-augmented systems for document-heavy, regulated domains. I’ve designed ingestion pipelines, implemented ontology layers for structured coverage and exclusions, and exposed clean, OpenAPI-documented endpoints for internal and partner use. I’ve also ensured outputs are auditable and compliant with industry rules. My approach will be to design a pipeline starting with data ingestion → normalization/ontology → exclusion mapping → RAG-powered retrieval → secure, versioned APIs. I will implement safeguards to reduce hallucinations and provide citations for every answer, ensuring trustworthiness in insurance workflows. The system will also allow traceability for regulatory audits. check our work https://www.freelancer.com/u/ayesha86664 Do you already have sample policy and claims documents for initial ingestion and testing? Let me know if you’re interested & we can discuss it. Best Regards Ayesha

@juanestebana17

Hi, I have extensive experience in developing LLM solutions and implementing retrieval-augmented generation (RAG) architectures for complex domains, making me well-equipped to build your insurance distribution layer. My approach will ensure that we can effectively structure and query multi-faceted insurance documents while providing clean, well-documented APIs. What specific challenges have you faced in insurance document processing that you'd like us to address? Best regards,

@mahads31

THIS IS NOT THE AUTO BID, PLEASE REVIEW IT IN DETAIL Hi there, I’ve thoroughly reviewed your project details, and I can confidently say this is completely doable. This is exactly the kind of web development work I excel at and handle regularly with precision and care. I’m a skilled web developer with strong experience in UML Design, PHP, Java, Large Language Model, Machine Learning (ML), SEO, Software Architecture, Compliance, API Development and Natural Language Processing. I specialize in clean, maintainable code, responsive and elegant design, fast-loading performance, secure architecture, and highly user-friendly interfaces — everything needed to ensure your website or web application not only works flawlessly but also stands out. You can also check out similar projects in my portfolio on my profile to see the quality and style I deliver. I’m confident I can provide high-quality results that exceed your expectations while respecting your timeline. Let’s turn your project into something amazing, sleek, and irresistible.

@GobindSawarn

I'm Govind, a seasoned developer specializing in Java with expertise in building reliable and scalable software - a perfect fit to lead your insurance distribution project. With a proven track record of transforming complex ideas into efficient solutions, I have the experience and skills necessary for this task. Having worked in regulated domains, I understand the importance of compliance and audit requirements, which is crucial for your project. My familiarity with LLMs and RAG models is backed by successful implementations on relevant projects that I can share. Moreover, my experience in the insurance sector will further expedite the project as I am well versed with concerns like policy administration and quote/bind processes. Significantly, my expertise in building API platforms with clean code practices complements the need for well-documented APIs that you've mentioned. I'm confident to not just execute your specified responsibilities adeptly but also bring added value with my ability to simplify complex processes and provide future-ready architectures. By hiring me, you'll be ensuring a smooth transition from document ingestion to retrieval via nuanced mappings and superior ontology structures. Let’s discuss more about your specific needs so we can outline an estimated timeline and team composition for an MVP!

@PinnacleDigitalS

✅ Hello I'm so interested in your "LLM Rag" project! If you want results, clarity, and a little less stress, I’m your person. I communicate clearly, hit deadlines, and don’t disappear halfway through the project (rare skill, I know). Looking forward to your response. Best regards

@TheDeveloper10

Hi, You’re looking to build a production-grade insurance distribution layer with LLMs and RAG for structured querying of complex documents, and I can help you implement this efficiently and cleanly. Here’s my approach: First, I’ll design ingestion pipelines for policies, quotes, endorsements, and claims, normalizing data and mapping coverages, exclusions, and limits into a coherent ontology. Next, I’ll implement a RAG-based retrieval architecture over this structured layer, ensuring explainable, citation-backed answers, and reducing hallucinations through document chunking, top-k retrieval, and strict context limits. Then, I’ll expose RESTful APIs with OpenAPI documentation, versioning, and authentication for internal and partner consumption, while embedding audit and traceability logs to meet regulatory standards. Finally, I’ll deliver a short architecture guide showing: ingestion → ontology → exclusions → retrieval → API endpoints, plus testing procedures for compliance. To proceed, could you clarify: Do you require multi-jurisdiction compliance rules from day one, or only for MVP? I have hands-on experience building RAG pipelines for legal and financial documents, with structured ontology layers and API-driven distribution. Let’s build a secure, auditable, and intelligent insurance query platform.

@kirills31

Hey there, What is the MVP document set and volume: policies endorsements claims in PDF DOCX, and do you need jurisdiction specific parsing (state country) from day one? For responses, do you require “answer only if cited” with section page references and a refusal mode when evidence is missing, or can the model add limited general guidance? I can build a production RAG distribution layer with an explicit ontology normalization tier: ingest documents, extract structured fields (coverage exclusion limit deductible conditions), map to a canonical schema, then serve both QA and API access with traceable citations. The key is auditability: every answer stores retrieved chunks, embedding ids, prompt version, model version, and a deterministic policy for confidence and refusal. I reduce hallucinations with retrieval first routing, tight context windows, structured outputs, eval suites, and citation enforcement that blocks uncited claims. Architecture outline: ingestion service + parser queue, ontology mapper service, vector store per tenant, retrieval API with rerank, QA orchestrator, OpenAPI gateway with auth versioning, and an audit store for traces. MVP can be 4–6 weeks with 1 engineer full time or 2 part time. Hope to discuss more on chat. Best, Kirill

@chiragardeshna

Hi there, I am a strong fit because I have built production-grade RAG systems for regulated domains involving long, complex documents and strict audit requirements. My MVP architecture would follow: document ingestion and structured extraction → ontology normalization for coverages/exclusions/limits → metadata-filtered vector retrieval → citation-enforced LLM responses in structured JSON → OpenAPI-documented REST endpoints with full audit logging. To reduce hallucinations, I use strict retrieval grounding, low-temperature prompts, answer validation against structured fields, and mandatory source citations with confidence thresholds. Estimated MVP timeline: 10–12 weeks with a small focused team (LLM/RAG + backend/API + data engineering). I can share relevant RAG and regulated-domain experience upon request. Regards, Chirag

@osamak74

Greetings, I’m excited about the opportunity to assist in building a sophisticated insurance distribution layer utilizing LLMs and RAG. My extensive experience with large language models and robust document processing enables me to effectively structure complex insurance documents and develop clean APIs. For your project, I propose the following architecture: 1. **Ingestion**: Develop scalable data pipelines to handle policies, quotes, endorsements, and claims efficiently. 2. **Ontology**: Implement a well-defined normalization layer to create a consistent framework for coverages and exclusions. 3. **Retrieval**: Create an explainable RAG model for accurate QA over documents, integrating safeguards for compliance and traceability. 4. **APIs**: Design OpenAPI documented endpoints to facilitate seamless access for both internal and partner systems. To minimize hallucinations, I recommend implementing stringent QA checks and citation protocols. I have led projects where we developed effective document parsing and API platforms within regulated domains, links can be provided upon request. I estimate a timeline of 3 months for the MVP with a compact team focused on delivering high-quality outputs.

@Hemantt841

I understand you require a senior engineer to build an insurance distribution layer using LLMs combined with retrieval-augmented generation for complex document querying and well-documented APIs. Your focus on ingestion pipelines for policies and claims, along with ontology mapping for coverages and exclusions, clearly sets a high bar for both accuracy and compliance. With over 15 years of experience and 200+ projects completed, I specialize in PHP and Java backend development, API design with OpenAPI standards, and NLP solutions tailored to regulated domains. My background includes building normalized data layers and RAG architectures to ensure explainable QA, especially in financial and legal contexts. For your project, I would design a pipeline starting with structured data ingestion, followed by ontology-based normalization of coverages and exclusions, then implement a RAG retrieval layer to minimize hallucinations by grounding answers in source documents. The API layer would include audit trails and compliance safeguards. A realistic MVP timeline would be 3 to 4 months with a small focused team. I’d be glad to explore your requirements in more detail and share examples of similar regulated LLM projects.