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You will get Design & Build a Production-Ready RAG Pipeline for Your LLM App

Med T.Status: Offline
Med T. Med T.
5.0
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Let a pro handle the details

Buy Generative AI services from Med, priced and ready to go.
Med T.Status: Offline
Med T. Med T.
5.0
Play video
Preview image

Let a pro handle the details

Buy Generative AI services from Med, priced and ready to go.

Project details

I build production-ready RAG pipelines that let your LLM answer questions accurately from your own documents, databases, or knowledge bases, not just from its training data. I designed and evaluated industrial RAG architectures for enterprise clients, benchmarking embedding models, vector databases, and retrieval strategies at scale. Whether you need a quick proof of concept or a fully deployed, monitored system, I deliver clean, documented source code with a working API endpoint. I work with LangChain, LlamaIndex, FAISS, Qdrant, vLLM, TensorRT, OpenAI, Mistral, LLaMA, and any custom stack you already have.
AI Algorithms
Feedforward Neural Network, Large Language Model, Transformer Model
AI Applications
AI Chatbot, AI Content Creation, AIOps, Conversational AI, Natural Language Generation, Natural Language Understanding
AI Development Language
Python
AI Tools
Hugging Face, NVIDIA AI Platform, PyTorch
AI Models
BERT, ChatGPT, GPT-3, GPT-4, GPT-J, LLaMA, Whisper
What's included
Service Tiers Starter
$250
Standard
$600
Advanced
$1,200
Delivery Time 5 days 10 days 21 days
Number of Revisions
123
AI Model Integration
Batch Normalization
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Database Integration
Detailed Code Comments
Image Upscaling
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MLOps
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Model Deployment
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Model Documentation
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Model Monitoring
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Model Testing & Optimization
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Model Tuning
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Natural Language Processing
NLP Tokenization
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Pre-Training
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Prompt Engineering
Setup File
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Source Code

Frequently asked questions

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EP

Emma P.
5.00
Oct 27, 2025
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Med T.Status: Offline

About Med

Med T.Status: Offline
LLM Inference & AI Systems Engineer
5.0  (1 review)
Caen, France - 4:20 am local time
I design and deploy production-grade AI systems: from RAG pipelines and vector search architectures to LLM inference optimization with vLLM and TensorRT-LLM. Currently at OVHcloud's Hardware/AI R&D team, I contribute to an industrial RAG system for their SREs, benchmarking open-source and cloud inference solutions at scale. I work across the full AI stack: data ingestion, embedding pipelines, NLP/NER model training, containerized deployment, and backend services. If you're building a serious AI system and need someone who has done it in production, not just in notebooks, I can help.

Steps for completing your project

After purchasing the project, send requirements so Med can start the project.

Delivery time starts when Med receives requirements from you.

Med works on your project following the steps below.

Revisions may occur after the delivery date.

Analyze your data sources, use case, and existing stack.

Design chunking strategy and select the optimal embedding model.

Review the work, release payment, and leave feedback to Med.