Hi, I'm Puneeth

I am a Full Stack Developer with 4+ years of experience delivering high-performance web applications across the complete Software Development Life Cycle. Completed my Master's in Computer Science at Saint Louis University with a strong GPA of 3.8.

I enjoy Chemistry, coding and finance. I am fascinated with machine learning and am quite proficient with most of the basic and well-known libraries available. I also adore coding and creating new things. I've worked with front-end and back-end web development, mobile applications, cloud technologies, and various programming languages and frameworks.

I enjoy playing cricket, volleyball and reading comic books and watching anime and explore new technology in my spare time.

Personal Information

Name: Puneeth Anantha
DOB: January 15, 2001
Email: a.puneeth.1501@gmail.com
Contact: +1 (636) 529-6770
Address: Saint Louis, Missouri, US
GPA: 3.8/4.0

Education

Saint Louis University

Master of Science in Computer Science

Saint Louis University, Saint Louis, Missouri

GPA: 3.8/4.0

Role: Graduate Teaching Assistant

Bachelor's Degree

Electronics & communication Engineering

Completed undergraduate studies with focus on computer science fundamentals, programming languages, data structures, algorithms, software engineering principles and core Electronics concepts

Professional Experience

PNC Financial

January 2025 – Present

Full Stack Developer (Contract)

St. Louis, MO

Developed and enhanced microservices with Spring Boot and Spring MVC, streamlining inter-service communication and modularizing business logic, which accelerated feature delivery by 30% across trading, wealth management, and investment platforms.

Engineered and deployed RESTful APIs for fund transfers, portfolio dashboards, and transaction history, optimizing queries and enabling response compression that improved API response times by 20%
Strengthened system security and regulatory compliance by implementing JWT tokenization, OAuth2, and fine-grained RBAC policies, reducing unauthorized login incidents by 42%
Architected RabbitMQ-based event-driven workflows for real-time fraud detection, credit evaluation, and fund disbursement, enabling processing of 50,000+ transactions per hour
Deployed and optimized AWS cloud infrastructure (ECS, S3, RDS, CloudWatch), leveraging lifecycle policies and automated backups that cut cloud costs by 25%

LTIMindtree

April 2020 – July 2023

Full Stack Developer

Hyderabad, India

Built patient-facing healthcare portals with React.js, Gatsby.js, and GraphQL, enabling over 15,000 monthly users to securely book appointments, manage prescriptions, and view medical history online, while reducing average navigation time by 40%.

Formulated backend microservices with Java 8/17, Spring Boot, and Hibernate to process over 2 million insurance claims annually, ensuring seamless scalability
Created RESTful APIs and GraphQL endpoints connecting EMR, pharmacy, and diagnostic systems, securely handling 250,000+ patient records daily
Implemented Apache Kafka pipelines for real-time processing of 50,000+ lab results and patient alerts per day, reducing average patient response time by 35%
Optimized relational and NoSQL databases (MySQL, PostgreSQL, MongoDB), supporting 10 TB+ of medical imaging data, reducing report generation time from 12 minutes to under 3

Projects

Medium Clone Platform

2025

Scalable Blog Platform Backend | Hono.js, Cloudflare, Prisma, TypeScript, Zod

Architected a scalable, modular RESTful backend using Hono.js, integrating JWT-based authentication, PostgreSQL via Prisma, and strict Zod schema validation, ensuring secure, maintainable, and reliable API interactions across the platform.

Formed and published a reusable Zod validation script to npm, minimizing runtime errors, enforcing consistent API schema standards, and improving developer efficiency while maintaining high reliability across multiple microservices.

github.com/puneeth1501/medium-clone

Warfarin Dosage Prediction

December 2024

Machine Learning Healthcare Solution | Python, Pandas, Scikit-learn, Google Colab

Constructed and optimized machine learning models (Linear Regression, Random Forest) to accurately predict personalized Warfarin dosage based on comprehensive patient data including age, weight, and genetic factors, significantly enhancing precision and safety in treatment recommendations.

Thoroughly preprocessed complex medical datasets, performed strategic feature selection, and applied cross-validation with hyperparameter tuning, substantially improving model reliability, performance, and practical applicability for real-world clinical scenarios.

huggingface.co/spaces/PuneethChowdary/warfarin

Smart Email Assistant

July 2025

Chrome Extension | React, JavaScript, Gemini API

Built a Gmail interface level Chrome Extension for AI email writing using React and Gemini API. The extension provides intelligent email composition suggestions and automated response generation to enhance productivity.

github.com/puneeth1501/smart-email-assistant

Research Assistant Extension

July 2025

AI-Powered Research Tool | Chrome Extension, React, Gemini API

Built a Side popup Extension for AI summarization and note saving locally using React and Gemini API. The tool helps researchers and students quickly summarize content, take notes, and organize information efficiently.

github.com/puneeth1501/research-assistant