You will get LLM API Integration for Your Python Backend (OpenAI, Claude)
Project details
I integrate OpenAI, Claude, or Gemini APIs into your existing Python backend without breaking what you already have.
You get production-ready code with proper error handling, cost tracking, and rate limiting — not just a demo that works once. The integration includes secure API key management, fallback logic when APIs fail, and clear documentation your team can actually use.
I've built LLM integrations for platforms handling 5000+ requests per day. I know where things break at scale and how to prevent it.
Works with FastAPI, Flask, or Django. You keep your current architecture — I add the AI layer on top.
Delivery: 4-5 days with full source code and setup guide.
You get production-ready code with proper error handling, cost tracking, and rate limiting — not just a demo that works once. The integration includes secure API key management, fallback logic when APIs fail, and clear documentation your team can actually use.
I've built LLM integrations for platforms handling 5000+ requests per day. I know where things break at scale and how to prevent it.
Works with FastAPI, Flask, or Django. You keep your current architecture — I add the AI layer on top.
Delivery: 4-5 days with full source code and setup guide.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Generated Code, Conversational AI, Machine Translation, Natural Language Generation, Natural Language Understanding, Sentiment AnalysisAI Development Language
PythonAI Tools
Azure OpenAI, GitHub Copilot, Hugging Face, Microsoft 365 Copilot, Replit, Streamlit, Word2vecAI Models
BERT, ChatGPT, GPT-3, GPT-4, LLaMA, OpenAI Codex, WhisperWhat's included
| Service Tiers |
Starter
$250
|
Standard
$400
|
Advanced
$800
|
|---|---|---|---|
| Delivery Time | 4 days | 8 days | 12 days |
Number of Revisions | 2 | 5 | 8 |
AI Model Integration | |||
Batch Normalization | - | - | - |
Database Integration | - | ||
Detailed Code Comments | |||
Image Upscaling | - | - | - |
MLOps | - | - | - |
Model Deployment | - | ||
Model Documentation | |||
Model Monitoring | - | - | |
Model Testing & Optimization | - | - | |
Model Tuning | - | - | - |
Natural Language Processing | - | ||
NLP Tokenization | - | - | - |
Pre-Training | - | - | - |
Prompt Engineering | - | ||
Setup File | |||
Source Code |
Frequently asked questions
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About Shivalaya
Python Engineer | LLM Applications, RAG & Agentic Systems
Shimla, India - 3:59 pm local time
My main background is backend development. So when I add AI to a system, it comes with proper auth, database design, async processing, and cost control. I do not just call the OpenAI API and hope it works. I build the full layer around it so it holds up in real use.
What I work on:
LLM integration for backends. I set up multi-LLM systems that route between OpenAI, Claude, or Gemini based on the task, with fallback when one model is down. I also wrap every LLM call so each request is monitored. This tracks tokens, cost, and latency, so you know exactly what your AI is spending and where.
Cost and performance. This is where most AI features go wrong. They work fine in the demo, and then the bill keeps growing. I add caching so repeated questions do not hit the API again, rate limiting and retry logic to keep it stable, and prompt handling to keep token usage low. The aim is simple: keep the system fast and the cost predictable as usage grows.
RAG systems. I use vector databases like pgvector or Pinecone, and I handle the document processing, chunking, embeddings, and semantic search. The answers come from your own data, not generic responses.
AI agents with LangChain and LangGraph. These are workflows where the AI decides which tools to call and when. I set up the agent logic, tool calling, and error handling so it does not break on unexpected input.
Backend integration is where most people struggle. I have integrated LLMs into FastAPI and Django apps with auth, rate limiting, background jobs (Celery and Redis), proper testing, and database schemas that fit AI content. I keep the APIs clean so frontend teams can work with them easily.
Stack: Python, FastAPI, OpenAI API, Claude API, LangChain, LangGraph, PostgreSQL, pgvector, Redis, AWS
Recent projects:
- Recruitment platform with AI resume screening, handling 50K+ API calls per day
- Clinical AI system on AWS Bedrock, with careful data handling for a regulated healthcare setting
- Voice AI assistant with real-time speech processing (Whisper, OpenAI, ElevenLabs)
What I focus on is reliability. AI systems that do not fall apart when traffic grows or edge cases show up. Monitoring, cost tracking, caching, rate limits, retries, testing, and error handling. This is the backend work that makes AI features reliable, not just good in a demo.
Available for longer projects (3 to 6 months).
Steps for completing your project
After purchasing the project, send requirements so Shivalaya can start the project.
Delivery time starts when Shivalaya receives requirements from you.
Shivalaya works on your project following the steps below.
Revisions may occur after the delivery date.
Requirements Review & Setup
Review your backend structure, LLM provider choice, and use case. Set up secure API key management and test connection to the LLM API.
Core Integration Development
Build FastAPI endpoints that connect to the LLM API. Implement request/response handling with proper data validation and type checking.