Featured Projects

Flagship AI Systems Project

Lane 1 — Applied ML

📊

2-Stage Loan Approval & Valuation System

● Built a two-stage ML workflow combining loan approval classification and loan value regression.

● Used Scikit-learn Pipelines and ColumnTransformer for structured preprocessing and reproducibility.

● Deployed a Streamlit dashboard for business-facing predictions.

Python Scikit-learn Pipelines ColumnTransformer Streamlit
📈

Academic Risk & Engagement Prediction

● Built a predictive analytics system to assess student academic risk and engagement potential.

● Performed EDA and feature engineering using demographic, academic, and behavioral data.

● Delivered an interpretable Streamlit dashboard for early-warning insights.

Python Scikit-learn Pandas Streamlit Predictive Analytics

Lane 2 — GenAI / RAG

🛡️

AI-Powered Customer Support Copilot

● Built a retrieval-augmented customer support assistant grounded in domain-specific knowledge.

● Designed a document ingestion and retrieval workflow to improve factual consistency and reduce hallucination risk.

● Created a support-focused AI interface with structured backend logic for scalable Q&A.

Python LangChain RAG FastAPI Streamlit Vector Retrieval
⚙️

LLM Web App with CI/CD Automation

● Built an LLM-powered web application with modern AI app delivery practices.

● Combined application-layer AI functionality with Dockerized workflows and CI/CD automation.

● Demonstrated repeatable AI application engineering beyond simple prototyping.

Python LLM Integration FastAPI Streamlit Docker CI/CD

Lane 3 — AI Systems / MLOps

👁️

Object Detection Pipeline with DVC & FastAPI

● Built an end-to-end computer vision workflow using Faster R-CNN with production-style MLOps practices.

● Implemented DVC pipelines and data/model versioning backed by cloud storage for reproducible ML workflows.

● Exposed the trained model through a FastAPI service and validated inference endpoints with Postman.

Python Faster R-CNN DVC FastAPI TensorBoard GCP Storage
🔍

PCAP StoryTeller

● Built a Python-based network analysis tool that converts packet capture data into structured, human-readable troubleshooting narratives.

● Designed to reduce manual investigation effort and improve support-team clarity during incident analysis.

● Highlights support-focused automation and systems-oriented problem solving.

Python Packet Analysis Data Parsing Automation Troubleshooting NLP Concepts