LLM Fine-Tuning & RAG Pipeline

@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 Python, and similar tools. I have worked with pytorch, and tensorflow to develop DL models, .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

@Mahihassi

As an experienced AI model developer with a strong background in Deep Learning, Machine Learning, and Natural Language Processing, I am confident in my ability to deliver precisely what you're looking for with your LLM fine-tuning and RAG pipeline project. Throughout my career, I've specialized in building robust and scalable backend systems that integrate the best of AI models with cloud infrastructure - which aligns perfectly with the needs of your project. I have prior experience working with open-source models such as Llama 3, Mistral, and Qwen - skills that are essential for your endeavor. Moreover, my proficiency in Python, FastAPI, Node.js, React, cloud platforms, and database systems can add great value to the intelligent chunking strategies, embedding models, vector-database integration and evaluation framework your project requires. In conclusion, I offer not only a comprehensive technical competence but a strong commitment to delivering clean, modular architectures capable of supporting production-ready applications. I am excited about this opportunity to contribute to your enterprise grade AI application by leveraging my knowledge in PEFT, QLoRA and fine-tuned weights on proprietary domain data. Let's discuss your specific requirements further and translate them into tangible results.

@maxscender

The core challenge is closing the accuracy gap between what open-source models achieve out of the box and what your proprietary domain requires. I will focus on the two critical failure points: catastrophic forgetting during fine-tuning and poor retrieval precision. By implementing parameter-efficient fine-tuning with a strict holdout set to monitor for drift, and building a modular RAG pipeline where chunking strategies are treated as a testable variable rather than an afterthought, the system will be built to quantify improvements. The evaluation framework will be developed in parallel with the pipeline, ensuring that every change to embeddings, vector stores, or model weights is measured against a baseline of retrieval quality and generation fidelity. The DeepSeek V4 relay is a practical cost lever that I will integrate as a standard API client, isolating it behind an interface so the system is never locked into a single vendor.

@toriquldev123

Hello There! I’m Md Toriqul Islam, an experienced AI/LLM engineer specializing in open-source LLMs, RAG pipelines, model fine-tuning, embeddings, vector databases, and production AI systems. I’m excited to partner with you and can start immediately. I understand you need engineers to optimize Llama 3, Mistral, and Qwen for proprietary domain data using PEFT/QLoRA, while building a modular production RAG pipeline with intelligent chunking, strong embeddings, vector search, and comprehensive evaluation. I’m skilled in Python, Hugging Face, PyTorch, PEFT, QLoRA, LangChain, vector databases, RAG, embeddings, model evaluation, and LLM API integrations. I’m ready to start immediately and can help establish a measurable fine-tuning and RAG workflow focused on quality, latency, scalability, and maintainability. Looking forward to hearing from you. Best regards, Md Toriqul Islam

@Hubready

Hello!, This is James from Hollywood... You’re not just looking for “an LLM engineer” here. You need someone who can turn an enterprise AI idea into a reliable pipeline that works in production, with clean retrieval, the right tuning strategy, and a setup that won’t become a maintenance headache. I can help by breaking this into clear phases: 1) audit the use case and define what should be fine-tuned vs handled by RAG 2) design the data prep and training set structure 3) build the vector DB and retrieval layer 4) fine-tune the model, test outputs, and tighten evals 5) package it for deployment with monitoring and iteration in mind I’ve built production AI systems, automation pipelines, and data-heavy apps where reliability matters, not just demos. If your goal is a serious enterprise-grade assistant, I’ll treat this like a system that has to earn trust. Relevant work includes AI copilots for an internal ops team, a document search assistant with RAG for a legal workflow, and a Mistral-based QA pipeline for structured knowledge retrieval. Quick questions: 1) What’s the main use case: support, internal knowledge, sales, or workflow automation? 2) Do you already have training data and docs, or should I help structure and clean them first? 3) Which stack are you planning for the vector DB and deployment layer? If you want, send me the current architecture and I’ll tell you exactly what I’d build first.

@ffulb

Most teams underestimate how much domain-specific fine-tuning degrades when retrieval context is noisy or when the vector store returns semantically close but factually wrong chunks. You end up with a model that hallucinates confidently because it learned your jargon but the RAG layer fed it garbage during inference. I'd handle that by building the eval framework first, not last. Measure retrieval precision at multiple k values, then use those metrics to tune your chunking strategy and embedding model before you ever touch PEFT. That way the fine-tuned weights and the RAG pipeline actually reinforce each other instead of fighting. I built an AI travel planner that routes multi-city itineraries through Claude with context injection, so I've wired up retrieval logic and prompt engineering at scale. I can spin up the QLoRA training loop, integrate your vector DB with proper reranking, and give you a clean evaluation suite that tracks both retrieval quality and generation accuracy. I'd want to take a look at your GPU setup, model storage, and the structure of your proprietary data first to confirm the chunking approach and whether we need custom embeddings or can

@invoketech

Hi, I understand you’re looking for experienced LLM engineers to work on open-source models such as Llama 3, Mistral, and Qwen, adapting them to proprietary domain data using PEFT/QLoRA and building a production-ready RAG pipeline. I recently worked on a similar LLM/RAG project involving model fine-tuning, embeddings, vector search, retrieval optimization, and evaluation, so I can contribute directly to the core engineering work. Working Flow Domain Data Preparation → Chunking & Embeddings → Vector DB Integration → RAG Pipeline → Llama/Mistral/Qwen PEFT/QLoRA Fine-Tuning → Retrieval & Generation Evaluation → Optimization → Production Deployment I can help build the system in a modular way with measurable retrieval/generation quality, efficient inference, and clean integration with your existing infrastructure. I’m also comfortable evaluating external model APIs/relays where they provide a practical cost and latency advantage. Thanks, Invoke Tech

@Shrutibimpl

Hello, I can fine tune your open source models and build your production grade RAG pipeline. My plan is to write Python scripts using Hugging Face PEFT and QLoRA to fine tune Llama 3 Mistral and Qwen models on your domain data. I can build a modular RAG pipeline using LlamaIndex or LangChain with semantic chunking advanced embeddings and vector databases like Qdrant or Milvus. I can set up an evaluation framework using Ragas or TruLens to benchmark retrieval recall and generation accuracy. I can also integrate your high performance DeepSeek API relay as a hybrid inference layer to optimize response speed and lower compute costs. In a past project I fine tuned Llama models using QLoRA and built a production RAG pipeline with Qdrant vector database and Ragas evaluation metrics. 1) Which specific vector database like Qdrant Milvus or Pinecone do you prefer for storing the document embeddings? 2) What evaluation framework like Ragas or TruLens do you want configured for measuring RAG quality? 3) Which GPU hardware or cloud provider like AWS or RunPod is available for running the PEFT QLoRA fine tuning jobs? Thanks, Bharat

@iterativeTech

With my extensive background in AI and ML, I believe I am uniquely positioned to optimize your existing models—Llama 3, Mistral, and Qwen—to match your proprietary domain data, enabling them to truly soar. As an AI and Cloud Data Engineering specialist, I have a deep understanding of techniques such as PEFT and QLoRA, which will be essential for adapting the weights of these models specifically to your knowledge base. Furthermore, your need for a production-ready Retrieval-Augmented Generation pipeline aligns perfectly with my skill set. Having worked across various industries, I have significant exposure to tackling complex data processing challenges using state-of-the-art technologies such as LLMs, PyTorch, TensorFlow, among others that are part of your technology stack. Particularly with regards to building modular pipelines for retrieval and generation tasks and adeptly integrating them with vector-database systems—I am well-versed. Finally, let me touch upon the infrastructure aspect you've highlighted. My experience with cloud-based architectures such as AWS Lambda or Azure's ML extends to optimizing cost-efficiency while maintaining high performance and security - which is consistent with the low-cost, high-performance API relay you mention. Choosing me means selecting a professional who can leverage your internal tools without compromising quality or increasing expenses. Let's talk about how we can integrate this into your workflow as per your needs.

@TeraByte26

Hi James, I will fine‑tune Llama 3, Mistral and Qwen on your domain data using PEFT/QLoRA and deliver a modular, production‑ready RAG pipeline with intelligent chunking, state‑of‑the‑art embeddings, vector‑DB integration and an evaluation suite. I can have a working demo ready in 10 days within your budget. Shall we start with a free sample of the fine‑tuned model? Regards, Alex Waiting for your response in chat! Best Regards.

@melopgabriel

Hi, your project needs two LLM engineers who can fine-tune open-source models and turn retrieval into a reliable production system. I can help with both sides: adapting Llama 3, Mistral, or Qwen with PEFT and QLoRA, and building a modular RAG pipeline that holds up under real use. I’ve worked on similar systems where the focus was domain adaptation, retrieval quality, and measurable performance. My approach is to start with data prep and model selection, then tune efficiently with QLoRA, and build the RAG stack around strong chunking, embeddings, vector search, and evaluation. I pay close attention to latency, maintainability, and clean interfaces so the system is easy to extend. If you want this handled with discipline and speed, I’d be glad to discuss the next steps. Best regards, Gabriel

@oussamaadam36

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@Aaleen110

The hardest part of this job is making open-source models perform on proprietary data, and it is the hard part because the data is unique. I would start by fine-tuning Mistral 7B using PEFT and QLoRA on your domain data, because Mistral is a strong foundation for this kind of adaptation and it gets the core LLM work done. For the RAG pipeline, I’d use LangChain for orchestration, implementing an intelligent chunking strategy with a fixed token window size and then embedding those chunks with the BGE-M3 model. The vector database would be ChromaDB, for its ease of integration and speed. I would build the RAG pipeline second so the fine-tuned model can be tested against a working retrieval system. What is the structure of the proprietary domain data you will be providing for fine-tuning? Track record on here: 100% on time, 100% on budget, 5.0 across 8 reviews. I need the specific format and volume of the proprietary domain data to finalize the plan.

@panos1414

Hi there , this is exactly the kind of build I enjoy: serious model work with real production stakes, not “move fast and hope the embeddings forgive us.” I can jump in on the LLM fine-tuning side with Llama 3, Mistral, and Qwen using PEFT/QLoRA, then help shape a clean RAG pipeline that’s modular, measurable, and ready for enterprise use. I’m comfortable designing chunking strategies, selecting embedding models, wiring vector DB integration, and building evaluation loops for retrieval and answer quality. I also like the practical angle here: low-latency infrastructure, domain adaptation, and a pipeline that doesn’t crumble the moment real users show up. If helpful, I can help define the model/data plan first, then move quickly into implementation and benchmarking. Best, Panagiotis

@najamulilsam

Hello, I see you need experienced LLM engineers to build and optimize an enterprise-grade AI application by adapting open-source models like Llama 3, Mistral, and Qwen for your proprietary domain data. The work includes PEFT/QLoRA fine-tuning, building a production-ready RAG pipeline, improving retrieval quality through intelligent chunking and embeddings, integrating vector databases, and creating evaluation systems to measure model performance. A few things I would like to clarify: 1. What is the current stage of the AI application, and are you looking to fine-tune existing models first or build the complete RAG architecture from the ground up? 2. What type and volume of proprietary data will be used for training and retrieval (documents, conversations, structured data, knowledge base, etc.)? 3. Which infrastructure are you currently using for model training and deployment (AWS/GCP/Azure, GPUs, Kubernetes, local infrastructure)? 4. Do you already have preferred technologies for the RAG stack (vector database, embedding models, orchestration framework like LangChain/LlamaIndex), or would you like recommendations based on your requirements? I would love to connect over a quick call to discuss your architecture, technical goals, and how we can help build a reliable enterprise AI solution. Najam SA!

@caodung171800

⮞⮞⮞⮞⮞ Dear client ⮜⮜⮜⮜⮜, Thank you for seeing my proposal. I have carefully reviewed your enterprise AI application project and I am confident I can deliver production-grade LLM solutions. -What are you looking to solve in this project? You need two experienced LLM engineers to fine-tune open-source models (Llama 3, Mistral, Qwen) with PEFT/QLoRA on proprietary data, build a production-ready RAG pipeline with intelligent chunking, embedding models, vector DB integration, and a robust evaluation framework. -What I can do for you in this project? I have deep experience with LLM fine-tuning (PEFT, QLoRA), RAG architectures, vector databases, and evaluation frameworks. I can build modular, battle-tested pipelines and optimise for performance. I am also interested in evaluating your DeepSeek V4 relay for cost-effective inference. ⚠️ If you want to solve more, I will do—advanced retrieval strategies, fine-grained evaluation, or deployment optimisation. Thank you for your time. I am confident I can deliver a high-performance, enterprise-grade solution. Best Regards

@georgethedev

Hi, You need LLM engineers who can go beyond basic fine-tuning and build a production-grade AI system around your proprietary domain knowledge. I can contribute across both model adaptation and RAG engineering, with a focus on measurable performance and maintainability. I can help with: Llama 3, Mistral, and Qwen model evaluation PEFT/LoRA/QLoRA fine-tuning Dataset preparation, cleaning, formatting, and quality control Domain-specific instruction tuning Intelligent chunking and document preprocessing Modern embedding model selection Vector database integration and retrieval optimization Hybrid/semantic search and reranking Production RAG pipeline architecture Retrieval and generation evaluation frameworks Hallucination, relevance, latency, and accuracy testing Modular pipelines that can evolve as your dataset grows I’d establish a baseline first, then benchmark models and retrieval strategies before fine-tuning. This makes it possible to clearly measure whether each change actually improves the system rather than relying on subjective outputs. If you share your domain, dataset format/size, current infrastructure, and target deployment environment, I can propose the model-selection, QLoRA, RAG, evaluation, and production roadmap immediately. Let’s get the first benchmark running and identify where the biggest performance gains are.

@godesignpk

Hi, I see you're building an enterprise-grade AI application and need help with LLM fine-tuning and a RAG pipeline. I specialize in AI model development and can assist with adapting models like Llama 3 and Mistral to your proprietary data. My approach includes implementing intelligent chunking strategies and ensuring seamless integration with vector databases for optimal retrieval performance. To kick things off, I suggest we review your current data structure and the specific requirements for your embedding models. What are your primary goals for retrieval quality and generation performance? Best regards, Waqas & GoDesign Team

@MuhammadUzair16

Hi, I am a software engineer with over 16 years of experience building production systems, including AI pipelines that must be measurable, maintainable, and reliable beyond a prototype. I can take ownership of fine-tuning Llama 3, Mistral, or Qwen on your proprietary data using PEFT/QLoRA, then build a modular RAG pipeline covering data preparation, intelligent chunking, embedding and vector-store integration, reranking, prompt orchestration, and evaluation. I would begin with a reproducible baseline, compare model and retrieval options against agreed quality and latency metrics, then package the best configuration with clean interfaces, tests, logging, and deployment documentation. This makes improvements evidence-driven and keeps the pipeline easy to extend. I can also evaluate the DeepSeek relay separately without coupling the core system to it. What are the approximate dataset size, target deployment environment, and preferred vector database? I’m available to start promptly and would be glad to discuss the details.