Browse categories
Explore
Fiverr Pro
English
$
USD
I will integrate llms into your python app using openai, gemini, or open source models
Manas J
About this gig
Already have a Python app? I'll make it intelligent.
I integrate large language models into existing applications cleanly, efficiently, and built for production. Whether you need a customer support bot, a document summarizer, a code assistant, or a multi-turn conversational interface, I've built it.
I've shipped LLM-powered systems using OpenAI, Google Gemini, and Anthropic Claude, including a voice-enabled AI assistant recognized at the Google GenAI Hackathon 2025. I've also built a 7-metric LLM evaluation framework to measure output quality so I don't just integrate models, I make sure they actually perform.
What you get:
LLM integration with your choice of provider
Streaming responses via SSE or WebSocket
Multi-turn conversation memory
Tool use and function calling
Clean FastAPI endpoints your frontend can call
Source code + documentation
Works with OpenAI, Gemini, Claude, Mistral, LLaMA, or any HuggingFace model.
Message me before ordering, every integration is different and I want to scope it properly.
Get to know Manas J
Manas J
Freelance AI Engineer
- FromIndia
- Member sinceMay 2026
- Avg. response time1 hour
Languages
Hindi, Oriya, English, Punjabi
I am an AI Engineer and Backend Developer specializing in RAG pipelines, multi-agent systems, and LLM fine-tuning. I have extensive experience building production-grade backends with FastAPI, Celery, and Docker, and I have successfully led teams in high-stakes environment to deliver complex ML solutions.
My Portfolio
FAQ
Which LLM providers do you support?
OpenAI (GPT-4o, GPT-3.5), Google Gemini, Anthropic Claude, and open-source models via Ollama or HuggingFace. I can also work with any provider that exposes an OpenAI-compatible API.
Can you add LLM capabilities to my existing codebase?
Yes, that's the primary use case. I'll integrate cleanly into your existing architecture without forcing a rewrite. I just need access to your repo and a description of what you want the LLM to do.
What's the difference between this and a RAG pipeline?
A basic LLM integration connects your app to a model for generation tasks — chat, summarization, classification. A RAG pipeline adds a retrieval layer so the model answers from your specific documents. If you need RAG, check my other gig.
Will the integration work in production or just locally?
Production. I deliver Docker-ready code with environment-based API key management, error handling, and rate limit awareness. Not a script that works on my machine.
Can you add evaluation so I know the LLM output quality?
Yes, as an add-on. I've built a 7-metric evaluation framework covering relevance, faithfulness, hallucination rate, and more. Message me if you want this included.
