You will get a production-ready multi-agent AI system with LangGraph or AutoGen
Project details
I build production-ready multi-agent AI systems using LangGraph and AutoGen — frameworks purpose-built for orchestrating autonomous AI workflows.
With 6+ years in AI/ML and deep expertise in LLM-based agent architectures, I design systems where multiple specialized agents collaborate: one researches, another reasons, a third executes — all coordinated through a graph-based state machine.
Every delivery includes:
• Agent workflow design & implementation (LangGraph / AutoGen)
• Tool integration (APIs, databases, web search, custom functions)
• Memory management (short-term context + long-term vector store)
• Error handling, retry logic & observability
• FastAPI deployment-ready codebase with documentation
I've built agentic solutions for RAG pipelines, document Q&A, automated research assistants, customer service bots, and data analysis agents — deployed on Azure, AWS, and GCP.
You'll receive clean, modular Python code that your team can extend and maintain.
With 6+ years in AI/ML and deep expertise in LLM-based agent architectures, I design systems where multiple specialized agents collaborate: one researches, another reasons, a third executes — all coordinated through a graph-based state machine.
Every delivery includes:
• Agent workflow design & implementation (LangGraph / AutoGen)
• Tool integration (APIs, databases, web search, custom functions)
• Memory management (short-term context + long-term vector store)
• Error handling, retry logic & observability
• FastAPI deployment-ready codebase with documentation
I've built agentic solutions for RAG pipelines, document Q&A, automated research assistants, customer service bots, and data analysis agents — deployed on Azure, AWS, and GCP.
You'll receive clean, modular Python code that your team can extend and maintain.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI Chatbot, Conversational AI, Natural Language GenerationAI Tools
Azure OpenAIAI Models
ChatGPT, LLaMAWhat's included
| Service Tiers |
Starter
$600
|
Standard
$1,400
|
Advanced
$2,500
|
|---|---|---|---|
| Delivery Time | 7 days | 14 days | 21 days |
Number of Revisions | 1 | 2 | 3 |
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 | - | - | - |
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Professional
Instantaneous
Always available
Quick work feedback and Logical. This is why I would recommend him. I work a organization as well I understand what a professional should work like. Kudos!
Instantaneous
Always available
Quick work feedback and Logical. This is why I would recommend him. I work a organization as well I understand what a professional should work like. Kudos!
About Sunny
Senior GenAI Engineer | LangChain | LangGraph | RAG | Agentic AI
Chandigarh, India - 8:06 pm local time
6 years building production-grade AI for Fortune 500 clients including Nike, Ernst & Young, and 7-Eleven. I architect and deploy complete AI systems — from first conversation to enterprise-scale production.
🔧 WHAT I BUILD
✔ Multi-agent systems (LangChain, LangGraph, AutoGen, LlamaIndex) — autonomous workflows that reason, plan, and act
✔ RAG pipelines with Pinecone, Weaviate, ChromaDB — grounded, accurate AI over your private data
✔ LLM integration and fine-tuning — GPT-4o, Claude, Gemini adapted to your domain
✔ Cloud-native deployments on Azure AKS and AWS Bedrock — Docker, Kubernetes, FastAPI, CI/CD
✔ NLP solutions — document processing, SQL generation from natural language, automated data extraction
🚀 PROJECT RESULTS
◆ ThreatSense AI (Nike) — Real-time cybersecurity multi-agent platform on AWS Bedrock. 40% faster incident response.
◆ Vision Flow (EY) — AI image-to-data pipeline on Azure AKS. 95%+ accuracy on complex document types.
◆ AI Ledger Analyzer (7-Eleven) — Natural language to SQL via Azure OpenAI. Eliminated manual financial query writing.
◆ QueryLens (Casey's) — Plain English to BigQuery on GCP. Enabled non-technical teams to query live data independently.
⚙️ TECH STACK
LangChain · LangGraph · LlamaIndex · AutoGen · Python · FastAPI · Azure OpenAI · AWS Bedrock · GPT-4o · Claude · Gemini · Pinecone · Weaviate · CosmosDB · PostgreSQL · Docker · Kubernetes · Databricks · Apache Spark · Kafka · MLflow
🌟 WHY CHOOSE ME
✔ Enterprise delivery — production systems used by global brands, not just demos
✔ Full-stack AI ownership — architecture, development, deployment, and monitoring
✔ Clear communication — you always know what's being built, why, and when
📩 Building an AI agent, RAG system, or LLM-powered application? Send me your project details. I respond within 4 hours.
Steps for completing your project
After purchasing the project, send requirements so Sunny can start the project.
Delivery time starts when Sunny receives requirements from you.
Sunny works on your project following the steps below.
Revisions may occur after the delivery date.
Agent Design & Development
Design agent roles, tools, and graph topology. Build the multi-agent system with LangGraph or AutoGen, integrate APIs and data sources, and deliver tested, documented Python code.