AI ML - EXPERT - MATH ML- IMAGES - FLOW CHARTS - SCIENCE - DATA - COMPETITIVE EXAMS - RAG - TRAINING

@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

@snbhanja

Hi, this is really interesting. I do have worked on custom RAG solution using open source models like Qwen, Deepseek etc with data ingestion and chat/answer apis. The output format can be according to your need, so that its easier to render in UI like Mathml or markdown or any format you want. Let me know.

@mohjib092

I can help with this. I have hands-on experience building RAG pipelines with Ollama, including Qwen 3 (8B-class models), and working with academic datasets. I’ve handled PDF ingestion for math-heavy content, combining OCR, layout parsing, and vision-capable models to extract formulas, diagrams, and flowcharts, then converting math content into clean, structured MathML. I can tune embeddings, chunking, and retrieval for mathematical reasoning, and handle image-to-math workflows so equations and visuals are preserved accurately.

@ionstefanache1

Hi, I can make this local AI project because I'm very comfortable with Math and AI. I have as GPU: RTX4060/8GB VRAM(which could support the LLM=Qwen3-VL:8B) and I could begin even now. Please contact me for more details The price will be updated in function of your complete requirement Thanks.

@Web1devloper

As an AI and ML expert with a unique proficiency in MATH ML, IMAGES, and FLOW CHARTS, I'm confident in my ability to tackle your project's requests. Given that your project primarily involves converting complex maths questions from PDFs into MATHML, my years of experience and technical prowess make me well-equipped to handle these academic data-related tasks. Building on my extensive knowledge of Python, I can proficiently optimize and convert math problems into MATHML formats. Beyond just comprehending the RAG model you're looking to implement (ESPECIALLY - RAG - OLAMA-QWEN 3:8B), I believe in going the extra mile for clients. This project is much more than just transforming questions; it requires a deep understanding of the material and how to represent it accurately. Having trained my machine learning algorithms extensively on such academic datasets, I can resolve any intricate equation or chart conversion ordeal you throw at me. My proven track record for delivering impeccable results within strict deadlines will ensure your project stays on track. From my practical experience in crafting dynamic web and app solutions to intricate data management and manipulation tasks, I possess the skills you require. Let us collaborate to achieve not only your desired results but also exceed your expectations!

@abdf2010

Hello there, I reviewed your project AI ML - EXPERT - MATH ML- IMAGES - FLOW CHARTS - SCIENCE - DATA - COMPETITIVE EXAMS - RAG - TRAINING and understood the requirements at a high level. I focus on delivering clear, stable, and maintainable solutions aligned with the actual scope, I can work with Python, Data Processing, Software Architecture and follow a clean development process with proper structure and error handling. If this aligns with what you’re looking for, please come to chat to discuss further. Best regards

@YashveerRathore

Greetings. I have reviewed your requirement and understand the need for AI and ML expertise focused on RAG for academic data. I am very confident that I can complete this work professionally and with strong attention to accuracy. The outcome will be reliable, structured, and suitable for academic use. I would be glad to discuss next steps and proceed.

@syedabdullahh7

Hi, I’m AI/ML & Full-Stack Developer specializing in Agentic AI systems and intelligent automation. My Portfolio is available on my profile. PRICE NEGOTIABLE ➡️ Fast delivery | 100% satisfaction

@mdabdullahalm72

Hi, I am an AI/ML expert specializing in RAG pipelines and Academic Data Processing. I have extensive experience with Ollama and Qwen models, making me a perfect fit for your project. What I will deliver: Custom RAG System: Using Ollama and Qwen 3.8B for your academic data. Math & Image Extraction: Accurately processing flowcharts and math images from PDFs. MathML Conversion: Ensuring all math questions are perfectly converted into MathML format. I am proficient in Python and advanced NLP techniques. I can start immediately and ensure high accuracy for your mathematical data. Looking forward to discussing this further. Best regards, Mamun

@dipakdev7351

Hi, I'm an experienced AI/ML engineer with deep expertise in RAG systems, NLP, and computer vision - exactly what this project needs. I have extensive hands-on experience with: ✓ RAG Implementation - Built production RAG systems using Ollama and various LLMs ✓ Qwen & Open-Source LLMs - Proven experience with Qwen models and local LLM deployment ✓ Document Processing - Converting PDFs into structured formats (MathML, JSON, etc.) ✓ Mathematical Content Recognition - Experience with mathematical expression extraction and conversion from images and PDFs ✓ Python & Data Pipeline Architecture - Building robust data processing pipelines for large-scale academic datasets **My Approach:** 1. Set up optimized Ollama environment with Qwen 3.8B for your use case 2. Build RAG pipeline to retrieve relevant academic content efficiently 3. Implement PDF parsing with specialized handling for math equations and flow charts 4. Convert mathematical notation to MathML with proper validation 5. Process images to extract diagrams and mathematical symbols 6. Optimize the system for accuracy and performance I understand the complexity of handling mathematical content and converting it to standard formats. I'm committed to delivering a production-ready solution that accurately processes your academic data. I'm available to start immediately and can discuss any specific requirements you have. What's the volume of data and priority timeline for this project? Best regards

@DrJagadeeswari

Hello, I specialize in RAG pipelines for academic & multimodal data, including PDF ingestion, Math content extraction, and vision-language processing. I’ve worked with Ollama-based models (including Qwen family), custom embeddings, vector stores, and structured retrieval for complex subjects like math, science, diagrams, and flow charts. What I’ll Build • RAG system using Ollama + Qwen 3:8B (or tuned variant) • PDF → structured pipeline for text, equations, images & diagrams • Math question extraction and conversion to MathML • OCR + vision-to-text for flowcharts & scientific figures • Chunking + embedding optimized for academic reasoning • Fast, accurate retrieval for competitive exam datasets Tech Stack Python, LangChain/LlamaIndex (as needed), Ollama, vector DB (FAISS/Chroma), OCR + vision models, custom parsers for MathML Focus Areas • High-accuracy math content handling • Multimodal RAG (text + images + equations) • Clean architecture for training & scaling ⏱ Timeline: 12 days ? Budget: ₹28,000 INR If helpful, I can first demo a small PDF → MathML → RAG prototype before full rollout. Best regards, Jagadeeswari.

@Tahoorr

I specialize in AI/ML systems that work with complex academic data, especially mathematics and machine learning content. I understand the challenges of parsing equations, symbols, images, and flowcharts from PDFs and preserving their semantic meaning in MathML. My experience with RAG architectures, vector databases, and multimodal pipelines allows me to build reliable, scalable solutions tailored for research and educational use cases. I focus on accuracy, clean architecture, and reproducibility—critical for academic workflows.

@Ashadb

I am specialized in RAG and ML Data Analysis and I have worked with academic research papers and also Pdf generation from project data in VLSI Projects.

@ketanm73

I need help building a Python-based AI/ML workflow, including data processing, automation, and API integration. The project should use clean, modular code and may involve tasks like model training, data analysis, automation scripts, and backend development. I want a reliable and well-structured solution with proper documentation

@mohamedaminea1

Hello, I am applying for your RAG-focused AI/ML project involving Ollama and Qwen 3:8B on academic math data. I have 5+ years of ML experience and recently built a RAG system over 120k academic PDFs, improving math-related retrieval precision by 38% and reducing hallucinations by 42%. In a similar project, I converted over 60k math questions from scanned PDFs to MathML using OCR + layout analysis, achieving 96% symbol accuracy and full support for formulas, flowcharts, and embedded images. My approach will follow these steps: (1) PDF ingestion with layout-aware parsing, (2) math OCR and formula segmentation, (3) MathML conversion and validation, (4) multimodal embedding and indexing, (5) RAG integration with Qwen 3:8B via Ollama. The system will use multimodal embeddings, vision-language alignment, structural math parsing, and evaluation-driven fine-tuning to ensure academic-grade accuracy and consistency. Do you already have labeled MathML ground truth or should it be generated during the pipeline? What is the expected scale of PDFs and the target latency for RAG responses? I am confident I can deliver a robust, production-ready RAG solution tailored to math-heavy academic content.

@harsh3531

I have hands-on experience building RAG pipelines using Ollama with Qwen models (including Qwen 3 8B) for domain-specific academic data. I can design an end-to-end solution to process math-heavy PDFs, extract text, equations, images, and flowcharts, and accurately convert math questions into structured MathML. My approach includes: PDF ingestion using tools like pdfplumber / PyMuPDF, with layout-aware parsing Image-to-text (OCR) for equations, diagrams, and flowcharts using vision models Mathematical expression normalization and LaTeX → MathML conversion RAG architecture with vector databases for retrieval over academic content Fine-tuned prompting and post-processing for math accuracy and consistency Python-based modular pipeline suitable for research and production use I’m comfortable working with ML/NLP pipelines, academic datasets, and mathematical content, ensuring high fidelity in equations and structured outputs. I can deliver a working prototype within 7 days, along with clear documentation and validation samples. Ready to start immediately and collaborate closely to meet academic accuracy standards.

@ayusht2010

Hello, I’ve worked on academic ML projects involving math-heavy PDFs, equations, images, and flowcharts. I understand the level of precision required—MathML must be correct, consistent, and usable, not approximate. I can deliver reliable MathML conversion from PDFs with clear validation and quick turnaround. If accuracy and academic correctness matter more than shortcuts, this will be handled properly. Happy to discuss sample input before starting. Thanks, Aayush

@arnabm63

I have a developed a RAG Chatbot already using langchain and llama3: 8b. The chatbot requires placing of the files in a directory from where it would read and embed using FAISS and langchain. Then it would invoke llama.

@Manisankar031

I am an AI/ML engineer with strong experience in building RAG pipelines using LLMs like Qwen, LLaMA and Open-source models on Ollama, combined with OCR, vision models, and document processing. I have worked on extracting structured information from PDFs including text, mathematical formulas, tables, and diagrams using tools such as Tesseract, PaddleOCR, LayoutLM, and vision-language models. For this project, I will build a complete pipeline to convert academic PDFs into structured MathML, extract equations, flowcharts, and images, and index them into a RAG system using vector databases for accurate retrieval and question answering. Qwen-3 8B will be integrated through Ollama for reasoning over math and scientific content. The system will support semantic search, equation-aware retrieval, and citation-based responses for competitive exam and academic queries. I will provide clean Python code, documentation, and a working demo within 10 days. I am confident I can deliver a production-ready and scalable solution.