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Virtual Nithin
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About Me
A little backstory on who I am and what makes me tick.
Who I Am
Hey there! I'm a software engineer who loves diving into diverse tech challenges. I've spent the last few years getting my hands dirty with everything from full-stack web apps to AI systems and cryptography. I work primarily with Python, Java, and TypeScript, but I'm always eager to pick up new languages and frameworks when the project calls for it.
I just wrapped up my B.Tech degree in Computer Science this spring (2025), but I've been building real-world apps throughout my studies. I'm particularly proud of my PC Builder platform that uses AI to help people choose compatible components, and my JavaScript post-quantum crypto library that's been downloaded over 1,000 times on NPM. Those projects weren't just academic exercises—they solved actual problems I cared about.
What gets me excited? Learning complex concepts quickly and turning them into something useful. I'm not just interested in writing code that works; I want to create solutions that make a difference. Whether I'm tweaking database performance with custom PL/pgSQL functions or designing intuitive UIs, I always keep the end user in mind.
When I'm not at the keyboard, you'll find me building custom PCs (yes, the GPU shortage!), tinkering with Linux distros, or catching the latest sci-fi film. I'm also passionate about making tech knowledge more accessible—we all start somewhere, right?
Interests & Hobbies
Education
B.TECH. in COMPUTER SCIENCE
2021-2025Vasireddy Venkatadri Institute of Technology (VVIT), Guntur, Andhra Pradesh
GPA: 8.28/10
My college journey where I not only mastered the CS fundamentals but also developed a passion for AI/ML and web development. Spent countless sleepless nights building projects that I cared about!
CLASS 12
2021Sri Chaitanya Junior College, Guntur
GPA: 94.7%
Those two intense years where I juggled PCM subjects and discovered my love for coding. Made some great friends and gained the discipline that still helps me today.
SSC - CLASS 10
2019Bhashyam High School, Guntur
GPA: 9.8/10
Where it all began! Got my first taste of programming here and never looked back. Those were simpler times, but they laid the foundation for everything that followed.
Skills & Expertise
I've acquired a wide range of skills throughout my journey as a software engineer and AI/ML developer.
Programming Languages
Additional Skills
Professional Certifications
AWS Academy Graduate
Amazon Web Services
2024
Google Cloud Certifications
2024
Deep Learning Specialization
DeepLearning.AI
2024
Machine Learning Specialization
DeepLearning.AI
2024
Google Professional Certificates
2024
Google IT Professional Certificates
2024
IBM Data Science Professional Certificate
IBM
2024
IBM AI Developer
IBM
2025
IBM AI Engineering
IBM
2025
Meta Front-End Developer Certificate
Meta
2024
Alteryx & Celonis Certifications
Alteryx & Celonis
2024
Cloud Platform Experience
Google Cloud Platform
Extensive hands-on experience via 50+ labs & 30+ Google Cloud Skill Badges, focusing on Machine Learning with Vertex AI (GenAI App Dev, Multimodal RAG, MLOps, Prompt Design, Vector Search), Data Engineering, and Cloud Architecture. Gold League on Skills Boost.
View Google Cloud Skills Boost Profile →
Amazon Web Services
AWS Academy Graduate: Completed advanced coursework in Cloud Foundations, Cloud Architecting, Machine Learning Foundations, and Data Engineering, gaining expertise in core AWS services (EC2, S3, RDS, IAM).
My Projects
Here are some of the projects I've worked on that showcase my skills and interests.
PC Building Assistant Platform
An intelligent platform that simplifies PC component selection through hybrid AI-powered recommendations and compatibility verification. Combines conversational interfaces (LLM) with traditional builder UI, backed by machine learning rankings, a comprehensive data pipeline (ETL), and a robust PostgreSQL database with embedded PL/pgSQL logic.
Technologies
Key Features
- Database-centric compatibility enforcement via PL/pgSQL stored functions
- ML-based component ranking using Gradient Boosting regression models
- Dynamic budget allocation algorithm optimizing component distribution
- Conversational UI powered by Ollama (Qwen 2.5 14B model)
Post-Quantum Cryptography Implementation
A pure JavaScript library implementing NIST-approved post-quantum cryptographic standards (ML-KEM, ML-DSA, SLH-DSA). Designed for web developer accessibility and high performance in JS environments.
Technologies
Key Features
- Full implementation of NIST FIPS 203, 204, 205
- High-performance: >2300 ML-KEM keygens/sec in JS
- NPM package with **1900+ downloads** (peak 331/week)
- Comprehensive documentation and research paper
PQC-Vizz
An interactive visualization platform for post-quantum cryptographic algorithms, making complex cryptographic concepts accessible and understandable through dynamic visual representations and step-by-step execution flows.
Technologies
Key Features
- Live in-browser execution of PQC algorithms from the 'pqc' library
- Step-by-step visualization of cryptographic operations
- Comparative analysis of PQC approaches
- Educational resource for developers & researchers
CareerPath Navigator
A user-centric digital platform empowering rural students with comprehensive career exploration tools, educational pathway visualizations, and personalized assessments, optimized for low-bandwidth environments.
Technologies
Key Features
- Mobile-first, responsive & accessible design
- Interactive career pathway & assessment tools
- Performance optimized for constrained devices/networks
- CI/CD deployment via Vercel
Math Minute
An educational Android application helping users improve mathematical skills through adaptive difficulty levels, timed exercises, and comprehensive performance tracking using Java and SQLite.
Technologies
Key Features
- Adaptive difficulty adjustment based on user performance metrics
- Multiple operation modes targeting different mathematical skills
- Offline functionality with local data persistence
- Performance analytics and progress visualization
London Bus Safety Analysis
A data analysis project that examines safety patterns in London's bus network, utilizing Python for data processing and Tableau for interactive visualizations to identify incident hotspots and temporal patterns.
Technologies
Key Features
- Comprehensive analysis of bus safety incident data
- Geospatial visualization of incident hotspots
- Temporal pattern identification and trend analysis
- Correlation analysis with external factors
Get In Touch
Interested in working together? Feel free to reach out using the contact form below.