Software Engineer with less than a year in AI/ML, IoT & Full Stack Development
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Assessing your cultural and operational fit
Results-driven Software Engineer with strong foundations in Algorithms and Object-Oriented Programming. Skilled in Python, Java, and SQL with hands-on experience in Machine Learning, IoT, and Full Stack Development. Passionate about designing efficient, scalable solutions and continuously learning emerging technologies in AI and Cloud Computing.
Mahatma Gandhi Institute of Technology (JNTU Hyderabad)
B.Tech · Electronics & Communication Engineering
August 1, 2019 – June 30, 2023
IoT-Based Forest Fire Alerting System with GPS
June 23, 2026 – Present
Designed an IoT-enabled early warning system that detects forest fire risk using temperature, gas, and humidity sensors connected through a NodeMCU (ESP8266) module. Real-time sensor readings were transmitted to a cloud dashboard for monitoring and alerts. Integrated a GPS module to capture exact fire coordinates, triggering SMS notifications to authorities through an API. Logged sensor data in Excel for threshold analysis and built a Power BI dashboard to visualize temperature, gas, and humidity trends over time, helping minimize false alarms and enhance detection accuracy. The system helps reduce environmental damage by ensuring quick response times to potential fires.
Algorithm Visualizer (DSA Project)
June 23, 2026 – Present
Created an interactive web tool that visualizes core data structures and algorithms like Sorting (Bubble, Merge) and Searching (Binary Search). Implemented using HTML, CSS, and JavaScript, leveraging the Canvas API for animation rendering. Designed real-time step-by-step visualization of algorithm logic and complexity for better conceptual understanding. Added speed controls, array size inputs, and color-coded steps for clarity and interactivity. Tracked algorithm runtime and comparison data in Excel and used Power BI to visualize performance differences between sorting and searching methods. This project demonstrates strong DSA understanding and frontend programming skills.
CNN-Based Tomato Leaf Disease Classification
June 23, 2026 – Present
Developed a deep learning model using Convolutional Neural Networks (CNNs) to detect and classify tomato leaf diseases from image data. The dataset was preprocessed with augmentation and normalization for better generalization. Achieved around 92% accuracy using TensorFlow and Keras, optimizing hyperparameters for improved model precision. Built a Flask-based web interface allowing users to upload leaf images and get instant predictions. Used Excel to organize and track model performance metrics across experiments, and built a Power BI dashboard to visualize accuracy trends and disease classification results for stakeholders. This project showcases practical application of AI in agriculture for early disease detection and yield improvement.
Cultural Fit Analysis
The candidate's academic projects demonstrate a diverse interest in AI, IoT, and DSA, indicating a willingness to explore different technical domains. The projects also show an application-oriented mindset, which aligns with a problem-solving culture. However, the lack of professional experience means cultural fit is primarily inferred from project diversity and self-reported strengths.
Soft Skills & Operational Fit
The candidate's resume highlights 'Strong problem-solving and analytical mindset', 'Rapid learner, adaptive to new technologies', and 'Excellent teamwork and communication skills'. The project descriptions are clear and well-structured, indicating good communication. The psychometric test score of 321/500 suggests average performance in areas like logical reasoning and work attitude, which could impact operational fit.