Hey there,
I'm Jeshwanth! 👋

I'm a ✓ Code Wizard who makes servers happy and browsers behave. By day, I battle bugs and architect cloud castles. By night, I train AI models to take over the world (just kidding... mostly).

I turn coffee ☕ into code 💻, problems 🤯 into solutions 💡, and requirements 📝 into reality 🚀. Let's build something amazing!

Ready to make magic happen?

Jeshwanth D
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Experience

Software Engineer

JPMorgan Chase & Co.

Aug 2024 – Present | OH, USA

Graduate Assistant

University of Cincinnati

Nov 2023 - Apr 2024 | Cincinnati, Ohio

Software Engineer

Infosys

Jan 2021 – Jun 2023 | India

Coding Profiles

LeetCode Logo LeetCode HackerRank Logo HackerRank CodeChef Logo CodeChef

Technical Skills

Programming Languages

Java
Python
C
C++
JavaScript
TypeScript
SQL
Shell Scripting

Backend Technologies

Spring Boot
Django
Node.js
Express.js
RESTful APIs
GraphQL
Microservices
RabbitMQ
Kafka
Spring Security
OAuth 2.0
JWT

Frontend Technologies

React.js
Angular
React Hooks
HTML
CSS
SASS
LESS
AJAX
jQuery

Database Systems

MySQL
PostgreSQL
MongoDB
Firebase
DynamoDB
Redis
Hibernate
SQLAlchemy

Cloud & DevOps

AWS (EC2, S3, Lambda, RDS, API Gateway)
GCP
Azure
Docker
Kubernetes
Terraform
CI/CD Pipelines
Jenkins
GitHub Actions
AWS CodePipeline

Monitoring & Logging

ELK Stack
Splunk
Prometheus
Grafana
AWS CloudWatch

Testing & Tools

JUnit
Mockito
Jest
Mocha
Selenium
Postman
Swagger
Bitbucket
Git
Maven
Gradle

Data Science & ML

TensorFlow
Scikit-Learn
Logistic Regression
CNN
BERT

Software Development

Agile
Scrum
Waterfall
Design Patterns
Microservices Architecture

Projects

Real-Time Physics & Rendering Engine Prototype

  • Designed and implemented a game-engine-inspired real-time simulation engine strong> with a fixed update loop and decoupled render pipeline.
  • Boosted Built physics-style computation modules to handle object movement, collision resolution, and state propagation across frames.
  • Implemented multithreaded execution strong> for simulation updates and rendering tasks, maintaining deterministic behavior under concurrency.
  • Optimized memory allocation and execution paths, reducing frame-time variance by 35% strong>and improving stability under sustained load.
  • Validated engine behavior using custom benchmarking tools and runtime instrumentation.

BookMyTrainer

  • Created a full-stack web application using Java Spring Boot (backend) and React.js (frontend) to allow users to find and book fitness trainers near their preferred location, integrating Google Maps API for location-based search and achieving 20% faster search results through optimized queries
  • Boosted backend performance by creating and refining stateless APIs using Java and the Spring framework, resulting in a 40% reduction in server-side latencies and improved overall application responsiveness
  • Implemented role-based access control using Spring Security and JWT authentication, supporting 3 user roles (Admin, Trainer, User), and integrated Stripe Payment Gateway for secure transactions, achieving a 95% payment success rate
  • Designed and managed a MySQL database with 10+ relational tables, ensuring data integrity for 500+ daily transactions, and documented REST APIs using Swagger, improving API testing efficiency by 40%
  • Built a responsive and interactive frontend using React.js and Material-UI, reducing page load time by 25%, and deployed the application on AWS, ensuring high availability and scalability
View Project

Full-Stack AI-Powered Application

  • Seamlessly integrated OpenAI’s GPT and DALL-E models, enabling chat, image generation, and recipe creation functionalities, reducing API response times by 30% through efficient token management and caching
  • Developed a real-time chat feature handling 100+ concurrent users, leveraging OpenAI’s GPT models to deliver accurate and context-aware responses with latency under 2 seconds
  • Created an image generation feature allowing users to customize image size, quality, and quantity, generating 4 images per request with a 95% success rate
  • Built a recipe creation feature that generates detailed recipes based on user inputs, reducing user input errors by 20% through input validation and adaptive prompt templates
View Project

Sentiment Analysis of Amazon Reviews Using BERT

  • Conducted sentiment analysis on Amazon reviews using advanced techniques including Bidirectional Encoder Representations from Transformers (BERT)
  • Achieved exceptional accuracy rates: 94% for multiclass classification, surpassing industry-standard models like Logistic Regression, Naïve Bayes, Random Forest, and SVM
  • Implemented Random Forest with word embedding, achieving an accuracy of 90% for multiclass classification
  • Enhanced customer decision-making by providing valuable insights from reviews
View Project

Contact

jeshwanthreddy210@gmail.com