Rithvik Golthi

Rithvik Golthi

Software Engineer & Full-Stack Developer

Passionate software engineer with expertise in distributed systems, AI integration, and cloud technologies. Currently building scalable solutions at Donato Technologies and developing innovative healthcare platforms.

Professional Summary

Experienced Software Engineer with 1+ years of expertise in building scalable distributed systems, AI-powered applications, and cloud-native solutions. Currently driving innovation at Donato Technologies, where I work with cross-functional teams in developing enterprise-grade microservices and implementing DevOps best practices.

My passion lies in creating intelligent healthcare solutions and cutting-edge AI systems. At DermaAI, I've architected HIPAA-compliant platforms that leverage LLMs and RAG architectures to deliver sub-second response times for critical medical applications.

Key Achievements

Performance Optimization

Reduced deployment time by 30% and load times by 30% through optimized CI/CD pipelines and frontend architecture

System Reliability

Achieved 99.9% data availability and reduced production outages by 45% through fault-tolerant architecture

AI Innovation

Developed AI-driven systems using GPT-4o, Claude, and Grok with RAG architecture for contextual medical queries

Team Collaboration

Led cross-functional collaboration with different teams in modern development practices

Education

Master of Science in Computer Science

University of Texas At Arlington

CGPA: 3.75/4.0 • Jan 2022 – Aug 2023

Specialization: Software Engineering & Big Data

Bachelor of Technology in Computer Science

Gokaraju Rangaraju Institute of Engineering and Technology

CGPA: 9.43/10 • Aug 2017 – Aug 2021

Professional Experience

Software Engineer

Donato Technologies

Oct 2023 – Present
  • Led cross-functional collaboration to develop an employee management system using Spring Boot microservices and React.js, enhancing role-based access control and orchestrating secure workflows with Apache Kafka for real-time inter-service communication.
  • Engineered scalable CI/CD pipelines using GitHub Actions and Docker, automating build, test, and deployment processes, reducing release time by 30%, and improving deployment reliability across environments.
  • Designed infrastructure-as-code using AWS CloudFormation, orchestrating containerized services with Kubernetes, enabling zero-downtime deployments and reducing production outages by 45%.
  • Built modular frontend components using React.js, TypeScript, and Tailwind CSS, incorporating code splitting, custom hooks, and lazy loading to reduce load times by 30%.
  • Automated unit and integration tests using JUnit, Postman, and Python, achieving 90% test coverage across critical APIs.

Software Engineer

DermaAI

Sep 2024 – Present
  • Independently designed and deployed a HIPAA-compliant distributed healthcare platform using Swift/SwiftUI, implementing secure patient management, medical documentation, and real-time status tracking.
  • Developed an AI-driven dermatology assistant using LLMs (GPT-4o, Grok, Claude) and RAG architecture, integrating Gemini embeddings and Qdrant vector DB for contextual patient queries with sub-second response latency.
  • Architected a fault-tolerant computing system using Firebase, AWS S3, and Cloud Functions, ensuring 99.9% data availability and implementing auto-scaling with global sync for platform-agnostic access.
  • Built a secure messaging system with differential privacy, role-based access, and encrypted data flow between patients and doctors, facilitating HIPAA-compliant real-time communication across devices.

Technical Skills

Programming Languages

Java Python JavaScript TypeScript SQL Swift C/C++ Kotlin

Frameworks & Libraries

Spring Boot React.js Angular Django REST APIs Kafka SwiftUI

Cloud & DevOps

AWS Docker Kubernetes GitHub Actions CI/CD Azure DevOps Firebase

Databases & Tools

MongoDB PostgreSQL SQLite Qdrant Git VS Code IntelliJ

Featured Projects

RAG Systems (Simple & Graph-based)

2024

Advanced Retrieval-Augmented Generation systems implementing both simple and graph-based approaches. Features semantic search, vector embeddings, and intelligent document retrieval for enhanced AI responses.

Beatzzz - Personalized Music App

2019

Developed a Django-based web application with Django Rest Framework (DRF) and RESTful APIs for getting and posting data, maintained SQLite database with responsive Bootstrap frontend.

FFTD - Friends, Family and Travel Diary

2023

Developed a dynamic family history platform using Java Spring Boot, MongoDB, and Kubernetes for orchestration, implementing family tree visualization, media management, and event tracking with Kafka for real-time updates.

Rotten Oranges - Movie Review Website

2020

Feature-rich movie review platform with user authorization, rating system, live database search, favorites functionality, and admin controls. Built with Node.js and MongoDB.

3D Object Detection for Autonomous Vehicles

2020

Implemented 3D semantic segmentation approach for object detection using pre-trained 3D-UNet model weights for predictions in autonomous vehicle applications.

Crop Weed Identification

2021

Used transfer learning with Xception model to identify 11 types of plant seedlings and crop-damaging weeds from 4750 training images, extracting bottleneck features for accurate predictions.

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