You will get a production RAG system with vector search and LLM
Project details
Build a production-ready RAG (Retrieval-Augmented Generation) system that connects your data to LLMs — enabling intelligent, context-aware answers over your own documents, databases, or knowledge bases. I design and deploy end-to-end RAG pipelines using LangChain, LlamaIndex, Pinecone, Weaviate, and pgvector, with hybrid search, re-ranking, and hallucination reduction techniques. With 6+ years in AI engineering and deep expertise in vector databases and LLM orchestration, I deliver RAG systems that are accurate, fast, and production-grade — complete with API, monitoring, and full documentation.
AI Algorithms
Large Language Model, Multimodal Large Language Model, Recurrent Neural Network, Transformer ModelAI Applications
AI Chatbot, AI Content Creation, AI-Enhanced Classification, Conversational AI, Natural Language Generation, Natural Language Understanding, Sentiment Analysis, Text RecognitionAI Development Language
PythonAI Tools
Azure OpenAI, Hugging Face, PyTorch, TensorFlowAI Models
BERT, ChatGPT, GPT-4, LLaMAWhat's included
| Service Tiers |
Starter
$800
|
Standard
$1,800
|
Advanced
$3,500
|
|---|---|---|---|
| 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 - 4:33 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.
Data Ingestion, Embedding & Vector Store Setup
Ingest your data sources, generate embeddings, set up the vector store, build the LLM query chain, and deploy a production-ready RAG API with retrieval evaluation.