You will get a multi-agent AI system using LangGraph or AutoGen
Project details
I build production-grade multi-agent AI systems using LangGraph and AutoGen, delivering intelligent automation that goes beyond single-model pipelines. With 6+ years in AI/GenAI engineering, I design agentic architectures where multiple specialized AI agents collaborate, reason, and execute tasks autonomously.
Each agent in the system has dedicated roles — planner, executor, critic, researcher — working together with shared memory, tool access, and state management. I implement human-in-the-loop controls, error recovery, and observability so the system is enterprise-ready from day one.
Whether you need a single-domain automation agent or a full enterprise agentic platform with RAG, monitoring, and CI/CD deployment, I deliver clean, documented, production-quality code with API endpoints and full handoff support.
Each agent in the system has dedicated roles — planner, executor, critic, researcher — working together with shared memory, tool access, and state management. I implement human-in-the-loop controls, error recovery, and observability so the system is enterprise-ready from day one.
Whether you need a single-domain automation agent or a full enterprise agentic platform with RAG, monitoring, and CI/CD deployment, I deliver clean, documented, production-quality code with API endpoints and full handoff support.
AI Algorithms
Large Language Model, Transformer ModelAI Applications
AI-Generated Code, Conversational AI, Natural Language Generation, Natural Language UnderstandingAI Development Language
PythonAI Tools
Azure OpenAI, Hugging FaceAI Models
ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$900
|
Standard
$2,000
|
Advanced
$4,000
|
|---|---|---|---|
| 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 | - | - | - |
2 reviews
(2)
(0)
(0)
(0)
(0)
This project doesn't have any reviews.
RK
Rajat K.
Jun 9, 2026
AI Developer Needed to Build an AI-Powered Application
DC
Deepika C.
May 15, 2023
Power BI Report Creation for 1 Million Rows Dataset
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 - 1:51 am 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.
Design, build & deploy agent system
Architect agent roles and workflow, implement with LangGraph/AutoGen, integrate tools and APIs, test agent loops, then deploy with FastAPI and CI/CD pipeline.