I will build llm powered rag application with fastapi and vector database


About this gig
LLM & RAG APPLICATION DEVELOPER
FastAPI · Python · Vector Databases · LLM Systems
I build production-grade AI systems that answer from your data (docs, PDFs, URLs, databases) using RAG and LLMs.
WHAT I BUILD
- RAG systems for document Q&A
- LLM API integration (OpenAI, Anthropic, Mistral, open-source)
- Vector search (Qdrant, Pinecone, Weaviate, ChromaDB)
- AI chatbots with memory & context awareness
- FastAPI backend services for AI apps
- Async AI pipelines for real-time processing
- Docker + cloud deployment (AWS, GCP)
⭐ WHY ME
- 4+ years building production AI/ML systems
- Working on AI systems at RTA Dubai
- 30+ ML projects delivered (4.9/5 rating)
- Strong focus on performance & production-ready code
TECH STACK
Python · FastAPI · LangChain · OpenAI · Qdrant · Pinecone · PostgreSQL · MongoDB · Redis · Docker · AWS · PyTorch · Ollama
PERFECT FOR
- AI chatbot using your own data
- SaaS AI features (search, Q&A, agents)
- Enterprise document intelligence systems
Message me before ordering to confirm scope.
Get to know Nasir I
Python AI Engineer LLM RAG Backend Developer FastAPI AWS Docker
- FromUnited Arab Emirates
- Member sinceMar 2025
- Avg. response time1 hour
Languages
English, Urdu, Spanish
FAQ
What data sources can the RAG system connect to?
PDFs, Word docs, websites/URLs, PostgreSQL/MySQL databases, CSV files, and APIs. If you have a custom data source, message me and I can assess integration feasibility.
Which LLM providers do you work with?
OpenAI (GPT-4o), Anthropic (Claude), Mistral, and open-source models via HuggingFace. I can also integrate with your existing LLM provider if you have one.
Will I own all the code?
Yes. Full source code with documentation is delivered. You own it completely and can modify or extend it however you need.
Can this be deployed to AWS or my own server?
Yes — all deliverables are Docker-ready. Standard and Premium packages include deployment guidance. Premium includes full AWS/cloud deployment.
