I am an engineering-focused developer who enjoys building software that is reliable, maintainable, scalable, and useful in the real world.
My work sits at the intersection of software engineering, AI/ML, backend systems, cloud tooling, and modern full-stack development. I approach products from both implementation and product perspectives: understanding the problem, designing the architecture, building the system, and continuously improving quality, performance, and developer experience.
I enjoy working across the complete lifecycle of a product — from system design and APIs to interfaces, data pipelines, deployment, observability, and iterative product development.
My engineering mindset is centered around:
- Software Engineering: clean architecture, maintainable code, APIs, data structures, algorithms, testing, debugging, and system thinking.
- AI / ML: practical machine learning, model integration, intelligent applications, data-driven workflows, and AI-powered product experiences.
- Full Stack Development: building cohesive products across frontend, backend, databases, APIs, and deployment.
- Product Engineering: balancing technical quality with usability, performance, reliability, and business impact.
Software Engineering · Backend Engineering · AI/ML Engineering · Full Stack Development · Product Engineering · Cloud & DevOps · Open Source
| Domain | Proficiency | Details |
|---|---|---|
| Machine Learning | Advanced | Supervised learning, feature engineering, model evaluation, preprocessing, and end-to-end ML workflows |
| Predictive Analytics | Advanced | Structured-data modeling, classification, regression, evaluation pipelines, and actionable predictions |
| Computer Vision | Intermediate | Image-based classification, object recognition concepts, preprocessing, and practical CV applications |
| Natural Language Processing | Intermediate | Text processing, embeddings, conversational systems, and AI-powered application workflows |
| Generative AI | Intermediate | LLM-powered applications, prompt workflows, API integration, and intelligent product experiences |
| Data Analysis | Advanced | Data cleaning, exploratory analysis, feature selection, visualization, and insight generation |
| AI Product Engineering | Advanced | Connecting models with APIs, interfaces, backend services, and production-oriented application architecture |
| Model Deployment | Intermediate | Packaging inference workflows and integrating machine learning capabilities into usable software systems |
01 · SoundWave — Spotify Style Music Player
A modern music-streaming interface inspired by the usability patterns of contemporary web music players. The project focuses on polished UI architecture, reusable components, responsive layouts, and a product-oriented user experience.
| Category | Details |
|---|---|
| Stack | React · JavaScript · CSS · Vite |
| Scale | Modular frontend architecture with reusable UI components |
| Performance | Component-driven rendering, optimized asset loading, responsive layouts |
| Security | Client-side validation and controlled application state |
| Impact | Demonstrates production-style frontend architecture and product UI engineering |
| Repository | View Repository |
The application emphasizes reusable components, clear visual hierarchy, scalable frontend organization, responsive behavior, and an interaction model that feels close to a production consumer product.
02 · Gemini AI Chat Application
A lightweight AI chat application designed around direct conversational interaction with Google's Gemini models. The application focuses on a clean chat experience, session continuity, API-driven responses, and simple configuration.
| Category | Details |
|---|---|
| Stack | Python · Streamlit · Google Gemini API |
| Scale | Session-based conversational workflow |
| Performance | Lightweight UI with direct model interaction |
| Security | API key handled through environment/session configuration |
| Impact | Demonstrates practical GenAI integration into an accessible product |
| Repository | View Repository |
The project explores how LLM capabilities can be embedded into a usable product without unnecessary infrastructure, while preserving a clean separation between configuration, application state, interface behavior, and model interaction.
03 · Wine Quality Prediction
An end-to-end machine learning project focused on predicting wine quality from structured chemical properties. It demonstrates the full ML workflow from preprocessing and exploratory analysis through model training and evaluation.
| Category | Details |
|---|---|
| Stack | Python · Pandas · NumPy · Scikit-learn |
| Scale | Structured tabular machine learning pipeline |
| Performance | Feature preprocessing, model evaluation, and inference workflow |
| Security | Controlled data ingestion and reproducible processing |
| Impact | Demonstrates practical supervised learning and model evaluation skills |
| Repository | View Repository |
The project highlights data quality, feature handling, reproducibility, model comparison, evaluation metrics, and the transformation of raw data into a predictive software workflow.
04 · Harshit Workout Tracker
A practical workout-tracking application focused on recording, organizing, and managing fitness activity through a simple software workflow.
| Category | Details |
|---|---|
| Stack | Python · Backend API · Database Integration |
| Scale | CRUD-oriented application workflow |
| Performance | Lightweight request and data persistence flow |
| Security | Controlled application input and backend data handling |
| Impact | Demonstrates backend-oriented application development and data management |
| Repository | View Repository |
The project focuses on practical backend engineering concepts including persistent data, application logic, API-oriented workflows, and designing software around an actual user problem.
05 · Driver Behavior AI
An AI-oriented project concept centered around understanding driver behavior using machine learning and intelligent data processing techniques.
| Category | Details |
|---|---|
| Stack | Python · Machine Learning · Data Processing |
| Scale | ML inference and behavioral analysis workflow |
| Performance | Feature-driven prediction pipeline |
| Security | Controlled input processing and model boundary design |
| Impact | Applies AI to a real-world behavioral and safety-oriented problem |
| Repository | View Repository |
The engineering objective is to translate behavioral signals into interpretable predictions while keeping the workflow modular enough to evolve into a larger AI-powered application.
06 · Developer Portfolio Directory
A curated developer-portfolio discovery platform designed to make it easier for engineers to find inspiration from high-quality personal websites and portfolio implementations.
| Category | Details |
|---|---|
| Stack | Markdown · GitHub · Web UI · Static Data Workflow |
| Scale | Continuously expandable portfolio directory |
| Performance | Static-first content architecture |
| Security | Repository-controlled content source |
| Impact | Helps developers discover portfolio patterns and improve their own personal branding |
| Repository | View Repository |
The architecture keeps content easy to maintain by using a single structured source while the presentation layer organizes portfolios alphabetically, supports direct navigation, and exposes a running portfolio count.
Ongoing
Engineering-focused project development across software applications, AI/ML workflows, backend services, and full-stack products.
Scope of Work
- Design and develop full-stack applications from concept through implementation.
- Build backend workflows, APIs, data persistence layers, and application logic.
- Develop AI/ML solutions for predictive and intelligent application use cases.
- Translate technical requirements into maintainable and user-focused software.
- Work across frontend, backend, data, deployment, debugging, and iterative product improvements.
- Apply software engineering fundamentals including modular architecture, reusable components, abstraction, testing, and version control.
- Build portfolio-quality projects with an emphasis on production-oriented engineering practices.
Skills
Java Python JavaScript TypeScript React Spring Boot Node.js SQL MongoDB AWS Docker Git Machine Learning Generative AI
| Recognition | Details |
|---|---|
| Engineering Projects | Built and iterated on software projects spanning AI/ML, web applications, backend systems, and developer tooling |
| AI / ML Development | Applied machine learning concepts to practical prediction and intelligent-application use cases |
| Full Stack Development | Built end-to-end applications across frontend, backend, database, and deployment layers |
| GitHub Engineering | Maintained a public engineering portfolio focused on practical, demonstrable implementations |
| Problem Solving | Continually strengthening data structures, algorithms, debugging, architecture, and software design fundamentals |
| Product Mindset | Focused on usability, maintainability, scalability, and real-world impact rather than code alone |
Learning:
- Advanced Java and backend engineering
- Data Structures and Algorithms
- System Design and scalable architectures
- Cloud-native development
- Advanced AI/ML and Generative AI
Building:
- Production-oriented full-stack applications
- AI-powered developer and productivity tools
- Backend services and API-driven systems
- Portfolio-grade software engineering projects
Exploring:
- Distributed systems
- AWS and cloud architecture
- LLM application engineering
- MLOps and model deployment
- Developer productivity and automation
Open To:
- Software Engineering opportunities
- Backend Engineering roles
- AI/ML Engineering opportunities
- Full Stack Product Engineering
- Open Source collaboration