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 Databases

PostgreSQL

MongoDB

tools

Git

Docker

AWS

Neo4j

Vercel

My Experience

Walmart

Advanced Software Engineering Intern Virtual Experience

Completed advanced software engineering simulations across Walmart teams. Implemented a heap-based priority queue in Java for shipping workflows, produced a UML class diagram for a data processor with multiple execution modes and database connectivity, and designed an ER diagram for a new accounting database based on business requirements.

BCG

Data Science Intern Virtual Experience

Built an end-to-end churn analysis workflow: data cleaning, feature engineering of behavioral/transactional drivers, and predictive modeling. Applied structured evaluation and error analysis to ensure insights were supported by measurable model performance.

Intelligent Systems Lab

Research Assistant (Applied ML & Intelligent Systems)

Conducting research in Retrieval-Augmented Generation (RAG) and graph-based reasoning. Developed modular RAG pipelines with semantic chunking and hybrid retrieval, reducing hallucinations and improving grounding. Built and optimized computer vision models in PyTorch and contributed to reproducible research workflows and documentation.