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Sanjay-00/README.md

Transforming Business Workflows into Intelligent Systems

About Me

MNNIT Allahabad 2025 · Currently at Shriram Finance · Building AI systems for real business workflows.

I work in portfolio analytics at a large NBFC. Most of what I've built here started as a real problem I ran into at work, information that was hard to access, workflows that were manual, decisions that needed better data and are real operational bottlenecks. I build to solve those problems, not to demonstrate technology.

Open to Applied AI, AI Engineering, and Data Analyst roles.

🛠️ Tech Stack

Analytics & Data

Applied AI & GenAI

AI Engineering & MLOps

Cloud & Infrastructure

Document Intelligence

Featured Projects

CollectionIQ - AI-powered portfolio intelligence system for collection leaders.

What it does

  • Upload portfolio data and ask questions in plain English
  • Generate portfolio summaries instantly
  • Surface collection insights and trends
  • Reduce dependency on manual reporting workflows

Why It Matters

Collection teams often spend significant time gathering information, analysing loan accounts, extracting information before making decisions. CollectionIQ reduces that friction by bringing insights directly to decision makers.

Tech Stack:

Python LangGraph Gemini Pandas Streamlit LangSmith

FinSight - AI-Assisted Underwriting Intelligence

Highlights

  • 15 deterministic underwriting and risk validation checks
  • Gemini-powered qualitative risk analysis
  • Rule-based + LLM hybrid architecture
  • RAG-powered borrower case Q&A
  • Focused on explainable AI-assisted credit workflows

Stack

Python • Gemini • LangChain • PyMuPDF • Streamlit

AutoCAM - Automated CIBIL Extraction System

Impact

  • Reduced manual processing from 30–60 minutes to under 1 minute
  • Supports CRIF High Mark and TransUnion formats
  • Validation against report totals
  • Gemini fallback pipeline for extraction correction

Stack

Python • PyMuPDF • Gemini API • OpenPyXL • Streamlit • LangSmith

Multi-Agent Blog Generation System

Agentic content generation workflow built using LangGraph orchestration patterns.

Features

  • Planning + orchestration architecture
  • Parallel worker execution
  • Tavily-powered research pipeline
  • Structured state sharing
  • Evidence-driven content generation

Stack

Python • LangGraph • LangChain • Gemini API • Tavily

Vehicle Insurance Prediction - End-to-End MLOps

Production-style ML pipeline with CI/CD and AWS deployment.

Features

  • Modular ML pipeline
  • Dockerized deployment
  • GitHub Actions CI/CD
  • AWS EC2 + ECR + S3 integration
  • FastAPI serving layer

Stack

FastAPI • Docker • AWS • MongoDB • GitHub Actions

Current Interests

  • Applied AI Systems
  • Agentic Workflows
  • Document Intelligence
  • Retrieval-Augmented Generation (RAG)
  • AI Reliability & Guardrails
  • Underwriting Intelligence
  • Collection Analytics
  • Workflow Automation
  • AI Product Systems

Connect With Me

Email LinkedIn GitHub Resume

Building AI systems that improve operational decision-making.

Pinned Loading

  1. CollectionIQ CollectionIQ Public

    An AI-powered loan portfolio dashboard built for Shriram Finance collection leaders. Upload a monthly LCC and the entire portfolio becomes instantly queryable in plain English, with automated risk …

    Python 1

  2. AI-Loan-analysis AI-Loan-analysis Public

    AI-assisted underwriting intelligence system for NBFC vehicle loan decisioning, combining deterministic risk validation with Gemini-powered qualitative analysis, document extraction, and RAG-based …

    Python 1

  3. AutoCAM AutoCAM Public

    Extracts structured loan account data from CRIF High Mark and TransUnion CIBIL PDFs and generates a formatted Excel file for credit analysts at Shriram Finance.

    Python 1

  4. Blog-writing-agent Blog-writing-agent Public

    Multi-agent AI content generation system using LangGraph with routing, retrieval, planning, parallel worker execution, and evidence-driven blog generation powered by Tavily and Gemini.

    Python 1

  5. MLOps--Vehicle-Insurance-Data-Pipeline MLOps--Vehicle-Insurance-Data-Pipeline Public

    Developed a fully modular and production-ready MLOps pipeline for predicting insurance policy responses, integrating CI/CD, containerization, and cloud services for robust deployment.

    Jupyter Notebook 1

  6. End-to-End-Azure-Data-Engineering-Pipeline-with-CICD End-to-End-Azure-Data-Engineering-Pipeline-with-CICD Public

    This is an end-to-end Azure-based Data Engineering pipeline following the Medallion Architecture (Bronze → Silver → Gold), built using tools like Azure Data Factory, Databricks, Delta Lake, Unity C…

    Jupyter Notebook