Data & AI Engineering | ETL & WarehousingANIMESH SINGH
🚀 Data Engineering & AI Infrastructure (ETL + RAG + Vector DB)
Build Scalable Data Systems & AI-Ready Infrastructure
I design and implement modern data engineering solutions-from ETL pipelines and data warehouses to AI-powered RAG systems using vector databases.
Whether you need structured analytics infrastructure or LLM-powered search and chat systems, I can build scalable, production-ready solutions.
Core Data Engineering Services
🔄 ETL / ELT Pipeline Development
Automated data pipelines
API, database & third-party integrations
Data cleaning & transformation
Batch & real-time processing
🏗 Data Warehouse & Architecture
Modern data stack setup
Snowflake / BigQuery / Redshift implementation
Data modeling & optimization
Performance tuning & cost control
☁️ Cloud & Orchestration
AWS / GCP / Azure deployment
Airflow / Prefect workflows
CI/CD for data pipelines
Infrastructure automation
🧠 RAG System Development
Build custom AI search/chat systems
Connect LLMs to your private data
Knowledge base chatbots
Document intelligence systems
📚 Vector Database Setup
Pinecone / Weaviate / Milvus
FAISS-based local vector stores
Embedding pipelines
Semantic search implementation
🗂 Document Processing
PDF / CSV / Database ingestion
Chunking & embedding strategies
Metadata filtering
Hybrid search (keyword + vector)
FAQs
Book a discovery call or send a message outlining your goals.
You’ll receive a clear technical roadmap and next steps.
I design and build scalable data systems - including ETL/ELT pipelines, cloud data infrastructure, data warehouses, and AI-powered RAG systems with vector databases.
From raw data to production-ready AI integration.
Yes. I develop secure RAG-based solutions that connect LLMs to your private documents, databases, or knowledge bases using vector search.
Perfect for internal assistants, AI search, or SaaS AI features.
Absolutely. I can audit, optimize, refactor, or scale your current pipelines and cloud infrastructure.
Python, SQL, modern cloud platforms (AWS/GCP/Azure), orchestration tools, data warehouses, and vector databases - chosen based on your scalability and cost goals.
Pipeline setup: 1-2 weeks
Data warehouse implementation: 3-6 weeks
RAG system deployment: 2- 4 weeks
Contact for pricing
Tags
AWS
Databricks
Python
SQL
data engin
RAG
snowf
sql
Service provided by
ANIMESH SINGH Delhi, India
Data & AI Engineering | ETL & WarehousingANIMESH SINGH
🚀 Data Engineering & AI Infrastructure (ETL + RAG + Vector DB)
Build Scalable Data Systems & AI-Ready Infrastructure
I design and implement modern data engineering solutions-from ETL pipelines and data warehouses to AI-powered RAG systems using vector databases.
Whether you need structured analytics infrastructure or LLM-powered search and chat systems, I can build scalable, production-ready solutions.
Core Data Engineering Services
🔄 ETL / ELT Pipeline Development
Automated data pipelines
API, database & third-party integrations
Data cleaning & transformation
Batch & real-time processing
🏗 Data Warehouse & Architecture
Modern data stack setup
Snowflake / BigQuery / Redshift implementation
Data modeling & optimization
Performance tuning & cost control
☁️ Cloud & Orchestration
AWS / GCP / Azure deployment
Airflow / Prefect workflows
CI/CD for data pipelines
Infrastructure automation
🧠 RAG System Development
Build custom AI search/chat systems
Connect LLMs to your private data
Knowledge base chatbots
Document intelligence systems
📚 Vector Database Setup
Pinecone / Weaviate / Milvus
FAISS-based local vector stores
Embedding pipelines
Semantic search implementation
🗂 Document Processing
PDF / CSV / Database ingestion
Chunking & embedding strategies
Metadata filtering
Hybrid search (keyword + vector)
FAQs
Book a discovery call or send a message outlining your goals.
You’ll receive a clear technical roadmap and next steps.
I design and build scalable data systems - including ETL/ELT pipelines, cloud data infrastructure, data warehouses, and AI-powered RAG systems with vector databases.
From raw data to production-ready AI integration.
Yes. I develop secure RAG-based solutions that connect LLMs to your private documents, databases, or knowledge bases using vector search.
Perfect for internal assistants, AI search, or SaaS AI features.
Absolutely. I can audit, optimize, refactor, or scale your current pipelines and cloud infrastructure.
Python, SQL, modern cloud platforms (AWS/GCP/Azure), orchestration tools, data warehouses, and vector databases - chosen based on your scalability and cost goals.
Pipeline setup: 1-2 weeks
Data warehouse implementation: 3-6 weeks
RAG system deployment: 2- 4 weeks
Contact for pricing