AI & Data Engineer with hands-on experience building agentic AI systems, RAG pipelines, and scalable data platforms.
Currently pursuing a Master of Applied Computing at the University of Windsor (Jun 2025).

I specialize in bridging robust data engineering with modern Generative AI to deliver reliable, production-ready systems.

• 🤖 Agentic AI & LLM Orchestration (LangGraph, LangChain, RAG)
• 📊 Distributed Data Engineering (Spark, Airflow, dbt, Kafka)
• ☁️ Cloud Platforms (Azure, AWS, GCP)
• 💾 Modern Analytics & Warehousing (Snowflake, PostgreSQL, BigQuery)
• 🧱 MLOps & Vector Databases (pgvector, Pinecone, MLflow)

• Multi-agent workflow using LangGraph
• Planner, Writer & Reviewer agents
• RAG + ChromaDB for contextual generation
• Streamlit UI

🔗 https://github.com/Chaitanya-0310/Multi_Model_AI_Agent

• End-to-end RAG pipeline
• FastAPI for real-time market data
• pgvector-enabled PostgreSQL
• Context-aware LLM responses

🔗 https://github.com/Chaitanya-0310/StockRAG-AI-Assistant

• Reddit API → Spark → Analytics
• Schema validation & deduplication
• Production-ready ETL design

🔗 https://github.com/Chaitanya-0310/RedditDataEngineerProject

• Built metadata-driven ingestion framework on Azure Data Factory
• Reduced onboarding time by 70%
• Optimized PySpark transformations using Z-Order & Liquid Clustering
• Reduced incremental load time by 35% and cloud compute cost
• Built dbt star-schema models for trusted analytics

• Modernized 12+ SSIS jobs → Airflow DAGs
• Built modular Python ingestion framework
• Improved query performance by 40%
• Implemented SCD2 pipelines
• Resolved 25+ data quality issues

• Automated feature extraction pipelines
• Built PowerBI & Streamlit dashboards
• Reduced ad-hoc reporting by 30%

• Databricks Lakehouse Fundamentals
• Databricks Generative AI
• Snowflake Hands-On Essentials
• MongoDB AI-Powered Search & RAG
• Apache Airflow Fundamentals

🚀 AI Engineer / Data Engineer roles (2025–2026)