GenAI RAG Engineer Document- Complex Document Parsing & AI Agent

@sajjadtaghvaeifr

Hello, I trust you're doing well. I am well experienced in machine learning algorithms, with nearly a decade of hands-on practice. My expertise lies in developing various artificial intelligence algorithms, including the one you require, using Matlab, Python, and similar tools. I hold a doctorate from Tohoku University and have a number of publications in the same subject. My portfolio, which showcases my past work, is available for your review. Your project piqued my interest, and I would be delighted to be part of it. Let's connect to discuss in detail. Warm regards. please check my portfolio link: https://www.freelancer.com/u/sajjadtaghvaeifr

@ashinnd84

Hi there, we are a team of Full Stack Web and Mobile App Developers. Please, send me a message to discuss the work and finish in no time. Thanks Ashish Kumar.

@mahads31

As an accomplished technology partner, I understand the importance of transforming complex ideas into efficient and long-term solutions, which makes me the ideal fit for your GenAI/LMM Application Engineer role. My expertise lies in Python and JavaScript, two key languages that are crucial for architecting an end-to-end RAG workflow, precisely what you require for your healthcare/life sciences PoC and MVP. But it's not just about the technical skills; understanding the needs of the specific domain is critical too. As a freelancer with an extensive track record across sectors, including healthcare and life sciences, I have a solid grasp on regulatory documents, metadata, and performing gap analysis - exactly the kind of complex document parsing you are looking for. My experience also includes working with AI automation processes which will be valuable to create an autonomous agent. Lastly, I pride myself on delivering more than just code. My meticulous documentation habits ensure that your project will be well-documented and reproducible while the detailed performance reports will keep you informed about latency, token usage, and error-handling strategies.

@nexvative

Hi, I can build the document-intelligence and gap-analysis GenAI platform with a production-focused RAG architecture designed specifically for complex healthcare/life-sciences regulatory documents. My approach would include: • Python + FastAPI services with LangChain/LangGraph for modular RAG and agent orchestration. • Robust PDF ingestion supporting scanned documents, OCR, dense tables, metadata, and varied layouts. • Intelligent chunking and hybrid retrieval using vector + keyword search with reranking for high-precision results. • Page-level and document-level citations so every generated answer can be traced back to the exact source. • Automated gap-analysis workflows against reference standards, with structured extraction, classification, comparison, and summarisation. • Agentic task routing with feedback loops for continuous evaluation and improvement. • Docker-based deployment with clear environment configuration, logging, error handling, and performance monitoring. I’ll deliver a clean Git repository, reproducible environment, deployed RAG service, demo covering at least three unseen documents, performance report, runbook, and handover session. I have strong experience with Python, RAG pipelines, vector databases, LLM integrations, document processing, OCR, and AI agent workflows. I can start quickly and work closely with your team to define milestones and measurable success criteria for the 3-month engagement.

@Abror86

Hi — Abror-Yakubov here from Uzbekistan, "RAG DOCUMENT INTELLIGENCE AGENT" — you need accurate answers from complex healthcare documents. I would build the pipeline around document parsing, chunking, embeddings, and retrieval with page-level citations. The key decision is preserving table structure and source references during parsing, because losing that context creates unreliable AI answers. I would keep the system modular with Python, LangChain-style workflows, vector storage, and clear APIs so models or data sources can change later. How many regulatory document formats need to be supported in the first PoC? Looking forward to working with you.

@hoangvanp

Hello, Understanding the project, the main goal is to automate the analysis of complex regulatory documents in the healthcare/life sciences field. The focus is on reducing manual work by ingesting multi-page PDFs, running gap analysis, and generating precise responses with citations. To achieve this, I would architect a production-ready RAG workflow in Python, integrating an LLM with a vector store. The parsing layer will handle varied layouts, feed content to the embedding store, and provide clear APIs. The autonomous agent will answer questions, route tasks, and improve through feedback loops, optimized for low latency and high accuracy. My experience includes similar AI projects in document parsing and workflow automation. A few questions: Which LLM do you prefer? Are there specific regulatory standards to focus on? Best regards,

@alluankit01

Hi, I can build the GenAI/RAG document intelligence PoC for complex healthcare and life-sciences documents, including PDF parsing, scanned text handling, table extraction, gap analysis, AI agent workflows, and page-level citations. The best solution is to first define the reference standards, document types, citation format, answer structure, and success metrics. I’ll then build a modular Python RAG pipeline using LangChain/LlamaIndex-style orchestration, OCR/table extraction, embeddings, vector store retrieval, LLM response generation, audit trail, and API endpoints. I’m comfortable with Python, LangChain, LLM applications, RAG, OCR, PDF parsing, dense tables, vector databases, document intelligence, healthcare/regulatory documents, Docker, APIs, latency optimization, and Git-based delivery. Deliverables will include: * Complex PDF/scanned document parser * Table and metadata extraction * Vector store and embeddings setup * RAG Q&A service * Gap-analysis workflow * AI agent for extraction/classification/summarization * Page-level citations and audit trail * Dockerized deployment * Sample queries on unseen documents * Performance report and runbook * Handover session I’ll focus on automation, accuracy, traceable citations, and clean architecture your internal team can extend after the PoC. Best regards Ankit

@jaroslavcaprata

Hi, This project goes well beyond a standard RAG implementation. The biggest challenge is building a reliable document intelligence pipeline that can accurately parse complex regulatory PDFs, preserve document structure, and produce traceable answers with page-level citations. The quality of parsing and retrieval will ultimately determine the quality of the AI agent. I would design the solution as a modular pipeline with robust document parsing (including scanned PDFs, tables, and metadata extraction), intelligent chunking, embeddings stored in a scalable vector database, and a LangChain-based orchestration layer. The agent would combine retrieval, reasoning, and task routing while maintaining audit trails, citation accuracy, and clear APIs for downstream applications. The deployment would be containerized with comprehensive documentation, monitoring, and evaluation to measure retrieval quality, latency, and token usage. Given the regulatory domain, I would also prioritize reproducibility, versioning of embeddings, and evaluation against representative documents to ensure the system remains maintainable as models or reference standards evolve. One question before defining the architecture: do you already have a preferred parsing stack (such as Azure Document Intelligence, Unstructured, Docling, or LlamaParse), or should selecting the optimal parsing approach be part of the engagement? Jaroslav Caprata

@rakhshanz3

As a seasoned AI engineer with deep-rooted expertise in Healthcare and Life Sciences, I am excited to offer you my unique combination of skills and experiences. With a PhD in Artificial Intelligence and prolific experience of nearly two decades, my technical capabilities extend from architecting end-to-end solutions to deploying cutting-edge AI algorithms. My most recent role as Chief Technology Officer at an AI-driven startup has afforded me extensive exposure to building custom AI tools for document processing, data extraction, and workflow automation - the precise skills needed in this project. Your PoC and MVP's successful completion depends heavily on possessing a well-rounded understanding of technology stack for efficiency and future adaptability. And that is exactly what you'll be getting if you choose me as your GenAI RAG Engineer Document Specialist. My highly collaborative approach paired with dedication towards producing clean, clearly documented codebases will ensure a seamless handover of deliverables to your internal dev team. Let's journey together towards automating complex healthcare document-processing leaving no room for errors!

@thiagofoz87

Your main challenge is not only building a RAG pipeline, but making document ingestion reliable enough for healthcare/regulatory workflows where traceability, citation accuracy, and structured extraction matter more than generic chatbot responses. I would approach this in layers: - Robust document parsing pipeline for scanned PDFs, tables, metadata extraction, OCR fallback, and layout-aware chunking. - Modular RAG architecture in Python using LangChain/LlamaIndex with swappable embedding and LLM providers. - Vector indexing strategy optimized for page-level citation recovery and auditability. - AI agent orchestration for classification, gap analysis, summarization, and task routing with feedback-driven refinement. - Containerized deployment with reproducible environments, API exposure, monitoring hooks, and performance instrumentation. For this type of workload, retrieval quality and chunking strategy are usually the difference between a convincing demo and a usable system. I would prioritize deterministic extraction, citation consistency, and measurable evaluation metrics from the beginning. The final delivery would include documented code, deployment artifacts, demo workflows on unseen documents, latency/token reporting, and a structured handover for your internal team.

@Ashoksahu001

Hello, Your project requires more than a standard RAG implementation—it needs a scalable, production-ready document intelligence platform capable of handling complex healthcare documents with high accuracy, traceability, and automation. I can build a modular AI solution that prioritizes reliable automation while remaining easy to extend as your requirements evolve. What I'll deliver: • End-to-end RAG pipeline using Python, LangChain/LlamaIndex, and a scalable vector database • Robust PDF parsing for scanned documents, tables, and structured metadata • Intelligent chunking, embeddings, and page-level citation tracking • Autonomous AI agent for extraction, gap analysis, classification, summarization, and Q&A • Modular REST APIs for seamless integration • Dockerized deployment with reproducible environment files • Performance optimization for latency, token usage, and retrieval accuracy • Comprehensive documentation, runbook, and knowledge transfer session My approach is milestone-driven: 1. Document ingestion & parsing 2. RAG architecture & vector store 3. AI agent with gap analysis workflow 4. API development & deployment 5. Testing, optimization, reporting & handover I'm available to start immediately and can work closely with your team throughout the engagement, delivering regular demos and ensuring the platform meets your automation, accuracy, and maintainability goals.

@ZohaibSathio

I build production RAG over regulated document sets - a private semantic search API across 100K+ PDF/DOCX at sub-500ms with source attribution and audit logging (95% top-3 relevance), and an autonomous agent that reasons over regulation and matched the human vote on 97% of 200+ board motions. Three things I would get right here: Page-level citations are a parsing contract, not a prompt instruction. You cannot ask the model to cite the page afterwards - the page number, region and table id have to ride with each chunk from the moment the PDF is split, and scanned pages need OCR that keeps coordinates. Retrofit it and every citation is a plausible guess. Dense tables break naive chunking. A table split at a chunk boundary loses its header row, and gap analysis is exactly the query that reads those cells. I extract tables as structured objects, serialised with their headers intact, separately from prose. Gap analysis is a recall problem, not a generation one. "What is missing" cannot be answered by top-k retrieval against the document - absence has no embedding. It runs the other way: enumerate the reference standard's requirements, then query the document per requirement, so an unmatched requirement is a real gap and not a retrieval miss. Portfolio: https://www.freelancer.com/u/ZohaibSathio. Which reference standards are you gapping against, and are the source PDFs native text or scanned? - Zohaib

@dhruvp0312

Hello, Your project is an excellent fit for my experience in building AI-powered applications with Python, LLMs, and RAG architectures. I can develop a production-ready PoC/MVP that automates document ingestion, gap analysis, and intelligent question answering with accurate page-level citations. My approach includes building a robust pipeline to process complex regulatory PDFs, including scanned documents, tables, and structured metadata, using OCR, LangChain/LlamaIndex, OpenAI or open-source LLMs, and vector databases like FAISS, Chroma, or Pinecone. The solution will be modular, scalable, and easy to extend with future models or data sources. I'll deliver a Dockerized application with clean APIs, reproducible environments, comprehensive documentation, and a well-structured Git repository. The AI agent will support automated extraction, classification, summarization, and feedback-driven improvements while maintaining low latency and high retrieval accuracy. I prioritize clean architecture, maintainable code, and transparent communication throughout the project. Although I'm not Hyderabad-based, I'm available to start immediately, collaborate closely, and meet your milestones on time. I'd be happy to discuss the architecture, implementation plan, and success metrics. I look forward to contributing to your healthcare document intelligence platform.

@vertechsolutions

Hi there, Complex regulatory PDFs with dense tables, scanned pages, and strict page-level citation requirements are preventing reliable automation and adding manual review overhead. I have spent the last 4 years solving exactly this type of problem. I will build a Python RAG pipeline (LangChain or equivalent) to parse varied layouts with OCR and table extraction, apply Natural Language Processing for semantic cleaning, generate embeddings into a FAISS/Milvus vector store, and expose Dockerized APIs. I will train lightweight Machine Learning (ML) classifiers for gap detection, implement an autonomous agent for routing and continuous feedback, and offer a Java-friendly adapter for enterprise integration. I will also add provenance, performance tuning, and low-latency optimizations. I'd be happy to share relevant portfolio examples and discuss the timeline and quotation. Best regards, syed ribal www.freelancer.com/u/vertechsolutions

@orionc10

I have hands-on experience building GenAI applications with RAG, LangChain, vector databases, and LLMs. I can deliver a scalable document intelligence solution that automates extraction, analysis, and question answering with reliable citations, clean code, and complete deployment documentation.

@dmytrot16

⭐⭐⭐⭐⭐Hello Are you tired of manual document analysis eating up your team's time in the healthcare sector? Imagine having an AI agent that not only handles complex regulatory documents but also automates gap analysis effortlessly. I prioritize quality over price. Guaranteed on-time delivery & 100% satisfaction! To tackle this challenge, I will architect a robust RAG workflow in Python, integrating your preferred LLM with a scalable vector store. By building a versatile parsing layer and clear APIs, we ensure seamless content extraction and classification. In a similar project, we developed a similar AI agent that reduced document analysis time by 60%, leading to a significant increase in operational efficiency for the client. Want to see a live demo of how this AI agent can revolutionize your document analysis process? Let's discuss the implementation roadmap and milestones to achieve your automation goals efficiently. Best regards

@enricos0

Hi there, Imagine a world where complex regulatory documents are parsed effortlessly, leaving the manual grind behind. That’s where I come in. With my experience in building AI solutions, I know how to create clean, professional workflows that make automation a breeze. I noticed you need an end-to-end RAG workflow that integrates a user-friendly LLM with a robust vector store. I’ve tackled similar challenges before, architecting solutions that optimize for low latency and high accuracy while maintaining flexibility for future updates. I specialize in Python and have a knack for developing seamless APIs that empower downstream applications. My commitment to speedy communication and quick turnarounds ensures that I’m always responsive to your needs and ready to deliver results. I am available for a quick chat! Regards, Enricos0

@bhargav922002

Hello, I'm bharghav, with 10 years of experience in developing robust Machine Learning solutions. My expertise spans Python, Java, JavaScript, and Machine Learning, making me well-suited for complex AI agent development. I understand your need for a GenAI RAG Engineer to build a specialized document intelligence agent for healthcare. I will architect a Python-based RAG workflow, integrating an LLM with an efficient vector store. The solution will include a robust parsing layer for complex PDFs and an autonomous agent for accurate, cited responses, prioritizing automation, speed, and accuracy.

@syeddev1998

Hi, I understand you're looking to build a reliable RAG-based AI agent that can accurately analyze complex regulatory documents, reduce manual effort, and provide traceable answers with page-level citations. I've been working with Python, Django, AI automation, RAG workflows, LLM integrations, and API-driven applications, with a strong focus on building scalable and maintainable solutions. My approach would be to first design a solid document processing pipeline, then build a modular RAG architecture that delivers accurate, citation-backed responses while keeping the system flexible for future models and data sources. I'd also recommend adding an evaluation pipeline to measure retrieval accuracy from day one, which makes future improvements much easier. I'm available to start immediately and would love to discuss your goals and the best approach for your MVP. Best regards, Syed Basit