GenAI Development — LLM Apps, RAG & AI Agents in PythonKishan Chauhan
I build GenAI features that run in production, on a real backend — not demos. LLM integrations, RAG systems (so answers stay grounded in your own data), chatbots, and agentic workflows built with LangChain and LangGraph. Because I'm a backend engineer first, the AI is wired into proper APIs, databases, auth, and error handling — with prompts, retries, and cost tracking handled so it holds up once real traffic and real data hit it.
LLM integration — Connecting OpenAI, Anthropic, or open models into your app, with prompts, retries, and cost/latency handling done properly.
RAG systems — Document Q&A and search grounded in your own data: chunking, embeddings, a vector store, and retrieval so answers stay accurate.
AI agents & workflows — Multi-step agents (LangChain/LangGraph) that use tools, call your APIs, and handle real tasks — not just chat.
Chatbots — Assistants over your content or product, connected to your backend.
The backend behind the AI — APIs, database, auth, and async jobs so the AI is a real part of your system, not a standalone script.
Documentation & handover — Clear setup, where to configure API keys, and how to run it.
FAQs
Starting at$20 /hr
Tags
FastAPI
LangChain
Python
AI Agent Developer
AI Chatbot Developer
Generative AI
Large Language Models (LLM)
Retrieval-Augmented Generation (RAG)
Vector Database
Service provided by
Kishan Chauhan Sahibzada Ajit Singh Nagar, India
- 6
- Followers
GenAI Development — LLM Apps, RAG & AI Agents in PythonKishan Chauhan
Starting at$20 /hr
Tags
FastAPI
LangChain
Python
AI Agent Developer
AI Chatbot Developer
Generative AI
Large Language Models (LLM)
Retrieval-Augmented Generation (RAG)
Vector Database
I build GenAI features that run in production, on a real backend — not demos. LLM integrations, RAG systems (so answers stay grounded in your own data), chatbots, and agentic workflows built with LangChain and LangGraph. Because I'm a backend engineer first, the AI is wired into proper APIs, databases, auth, and error handling — with prompts, retries, and cost tracking handled so it holds up once real traffic and real data hit it.
LLM integration — Connecting OpenAI, Anthropic, or open models into your app, with prompts, retries, and cost/latency handling done properly.
RAG systems — Document Q&A and search grounded in your own data: chunking, embeddings, a vector store, and retrieval so answers stay accurate.
AI agents & workflows — Multi-step agents (LangChain/LangGraph) that use tools, call your APIs, and handle real tasks — not just chat.
Chatbots — Assistants over your content or product, connected to your backend.
The backend behind the AI — APIs, database, auth, and async jobs so the AI is a real part of your system, not a standalone script.
Documentation & handover — Clear setup, where to configure API keys, and how to run it.
FAQs
$20 /hr