Portfolio - Vishal Krishna Kumar

I develop intelligent ML systems with a strong focus on accuracy, explainability, and robust evaluation, ensuring reliable performance through rigorous experimentation.

This is me.

Hi, I'm Vishal 👋

I'm a Machine learning developer pursuing master's in Computer Science specialized in Data Science focused on building intelligent, scalable, and production-ready systems I enjoy translating complex problems into practical, well-designed technical solutions with real-world impact.

Currently, my interests are centered around Generative AI, Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and data-driven system design. I am currently focused on strengthening my skills and actively looking for Summer 2026 internship opportunities.

My Stack

ml_ai

Machine Learning

Deep Learning

NLP

Computer Vision

RAG

GraphRAG

frameworks

PyTorch

TensorFlow

Scikit-learn

OpenCV

Hugging Face

LangChain

database

MySQL

Vector DataBase

PostgreSQL

MongoDB

tools

Git

Docker

AWS

Neo4j

Vercel

My Experience

Walmart

Advanced Software Engineering Virtual Experience .

Completed advanced software engineering simulations across multiple Walmart teams. Implemented a novel heap data structure in Java for shipping workflows, demonstrating strong algorithmic problem-solving. Produced a UML class diagram for a data processor supporting multiple operating modes and database connections, and designed an ER diagram for a new accounting database based on business requirements.

BCG

BCG - Data Science intern Virtual .

Engineered an end-to-end data pipeline for a customer churn simulation, involving rigorous data cleaning, feature engineering of transactional drivers, and predictive modeling. Employed structured analytical thinking to validate model accuracy and ensure findings were rooted in statistically significant behavioral triggers.

Intelligent Systems Lab

Research Assistant Intelligent Systems .

Leading research in Retrieval-Augmented Generation (RAG) and graph-based reasoning systems. Developed modular RAG pipelines with semantic chunking and hybrid retrieval, reducing hallucinations by ~35%. Implemented computer vision models using PyTorch, achieving ~25% performance gains through architecture optimization.Contributed to open-source AI frameworks.