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BalajiV21/README.md

Hi, I'm Balaji Viswanathan

AI/MLEngineer | LLMs & Agentic AI Systems | RAG Pipelines

I design and build intelligent AI systems - from autonomous agentic workflows to production-ready RAG pipelines. Currently completing my Post Graduate degree in AI & ML at Nipissing University, Ontario, Canada.

What I Work On

  • Agentic AI Systems — Multi-agent workflows with planning, tool use, memory, and self-reflection using LangGraph
  • RAG Pipelines — Hybrid retrieval (semantic + keyword + metadata) with FAISS, ChromaDB, Pinecone
  • LLM Engineering — Prompt engineering, fine-tuning, and integrating OpenAI, Ollama, and Hugging Face models
  • AI Automation — ETL-integrated LLM agents, data quality copilots, and intelligent document analysis

Tech Stack

AI/ML Python PyTorch LangChain HuggingFace OpenAI

Databases & Infrastructure FastAPI Docker Redis Neo4j Azure

Pinned Loading

  1. Agentic_Compliance_Auditor Agentic_Compliance_Auditor Public

    An advanced regulatory compliance auditing system that helps organizations automatically analyze documents for compliance requirements using autonomous agents, enhanced memory systems, and intellig…

    Python

  2. AI-Powered-Research-Assistant-for-Web-Articles AI-Powered-Research-Assistant-for-Web-Articles Public

    This project is a streamlined, AI-powered research tool designed to gather, process, and analyze information from online articles. Built with LangChain, Streamlit, and FAISS, it allows users to inp…

    Python

  3. Agentic_dataQuality_copilot Agentic_dataQuality_copilot Public

    LLM-enhanced Data Engineering project showcasing an Agentic AI workflow. The system ingests data via Azure Event Hub + ADF, processes it in Databricks using Delta Lake, performs data quality checks…

    Python 1

  4. Amazon_SentimentAnalysis Amazon_SentimentAnalysis Public

    This repository implements sentiment analysis on Amazon product reviews using VADER (Valence Aware Dictionary and sEntiment Reasoner) and RoBERTa (A Robustly Optimized BERT Pretraining Approach). …

    Jupyter Notebook

  5. Post-hoc_interpretability_ML_in_TimeSeriesClassification Post-hoc_interpretability_ML_in_TimeSeriesClassification Public

    Interpretable ML for Time Series

    Python

  6. Customer-Insights-and-Churn-Analysis- Customer-Insights-and-Churn-Analysis- Public

    Build an interactive Power BI dashboard to analyze customer demographics, service usage, and churn trends. Includes KPIs like churn rate, revenue, and tenure. Features interactive filters, insights…