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You will get a production RAG pipeline with FastAPI and your choice of vector DB

Amr B.Status: Offline
Amr B.
Preview imagePreview imagePreview imagePreview image

Let a pro handle the details

Buy Generative AI services from Amr, priced and ready to go.
Amr B.Status: Offline
Amr B.
Preview imagePreview imagePreview imagePreview image

Let a pro handle the details

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

Project details

Most RAG pipelines look great in demos and fall apart in production — wrong chunk sizes, no caching, embeddings that don't match the query style, hallucinations with no source grounding. I've built production RAG systems from scratch: VectorLens (ingestion cut from 181s to 0.03s), a multilingual Arabic/English telecom chatbot with hybrid BM25 + vector retrieval, and DocuMind, a knowledge graph-powered document intelligence platform. You'll get clean, documented, Docker-ready code — not a Jupyter notebook. I own the delivery end-to-end.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer Model
AI Applications
AI Chatbot, AIOps, Conversational AI, Natural Language Generation, Natural Language Understanding
AI Development Language
Python
AI Tools
Gradio, Hugging Face, PyTorch, Replit, Streamlit
AI Models
BERT, ChatGPT, LLaMA
What's included
Service Tiers Starter
$200
Standard
$450
Advanced
$1,000
Delivery Time 5 days 7 days 14 days
Number of Revisions
233
AI Model Integration
Batch Normalization
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Database Integration
Detailed Code Comments
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Image Upscaling
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MLOps
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Model Deployment
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Model Documentation
Model Monitoring
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Model Testing & Optimization
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Model Tuning
Natural Language Processing
NLP Tokenization
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Pre-Training
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Prompt Engineering
Setup File
Source Code

Frequently asked questions

Amr B.Status: Offline

About Amr

Amr B.Status: Offline
RAG Pipeline & LLM Systems Engineer | ChatBots | Document AI | MLOps
Kafr ash Shaykh, Egypt - 9:16 pm local time
Building an AI chatbot, document search system, or LLM-powered product but struggling with hallucinations, slow retrieval, or a prototype that doesn't hold up in production?
That's exactly what I fix.
I'm an ML engineer specializing in RAG pipelines, LLM fine-tuning, and production MLOps. I don't just wire up an API call to GPT-4, I build systems that are fast, accurate, and actually deployable.
What I've shipped:
→ VectorLens: Cut document ingestion from 181s → 0.03s using async Celery workers + Kafka event pipelines (Qdrant, ChromaDB, FastAPI)
→ CodeGuard-7B: Fine-tuned a 7B LLM with DPO to 92.8% vulnerability detection accuracy, then cut inference latency 40% with semantic caching
→ Rouh (PureSoft): Built a production RAG search pipeline for an e-commerce platform 30% faster product discovery, 25% latency reduction, 100% interaction logging
I work well with founders and teams who need someone who can own the AI layer end-to-end, from chunking strategy and embedding choice to deployment and monitoring.
If you need a RAG pipeline, a fine-tuned model, an LLM API integration, or a chatbot that actually works, let's talk.

Steps for completing your project

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

Delivery time starts when Amr receives requirements from you.

Amr works on your project following the steps below.

Revisions may occur after the delivery date.

Scope Q&A — data sources, LLM, retrieval requirements

Ingestion — chunking, embedding, vector store setup

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