Hi, I'm Ameda Rohith

AI & Machine Learning Engineer | Full-Stack Developer | Problem Solver

Building intelligent applications with AI, Machine Learning, Deep Learning, and Full-Stack Development.

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About Me

I'm Rohith,

I am an AIML undergraduate at SR University with strong foundations in Python, Data Structures, SQL, Machine Learning, Deep Learning, and Full-Stack Development. I enjoy building scalable AI-driven and web-based applications that solve real-world problems. My interests include Artificial Intelligence, Generative AI, Explainable AI, Cloud Computing, and Software Engineering.

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Education

SR University, Warangal

B.Tech in Computer Science (Artificial Intelligence and Machine Learning)

2023 – 2027

CGPA: 9.446 / 10

Ranked among the Top 5% students in the AIML program through consistent academic performance.

Skills

Technical Skills

My technical expertise and core engineering competencies:

Programming & Databases

Python Java SQL C PostgreSQL MongoDB

Machine Learning & AI

Machine Learning Deep Learning LSTM / GRU / CNN-LSTM Explainable AI (SHAP, LIME)

Web & Frameworks

HTML CSS JavaScript Flask Rest APIs

Libraries, Tools & Core CS

NumPy / Pandas / Scikit-Learn Git & GitHub

Data Structures & Algorithms OS / Computer Networks

Projects

1. AI Code Debugger

Python | Flask | OpenRouter API | LLMs

Built an AI-powered debugging assistant that analyzes source code and detects syntax, logical, and runtime issues. Automated code reviews by generating feedback, optimization suggestions, bug fixes, and quality scores. Deployed a Flask-based application leveraging LLM APIs for real-time code analysis.

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2. Flood Guard

HTML | CSS | JavaScript

Designed a flood-risk monitoring platform supporting multiple stakeholder roles and disaster management modules. Implemented emergency coordination, alert management, evacuation tracking, and reporting features. Architected a scalable framework integrating real-time monitoring, predictive analytics, and automated alerts.

Runner-Up at UDBHAVA X'26 Hackathon

3. Air Pollution Forecasting

Python | Deep Learning | SHAP | LIME

Developed deep learning models to predict PM2.5 levels using meteorological and pollution data. Achieved R² scores up to 0.92 with LSTM outperforming GRU and CNN-LSTM models. Integrated Explainable AI techniques to improve transparency and identify key environmental factors influencing predictions.

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