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AI Engineer with less than a year in Machine Learning & Mobile App Development
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Assessing your cultural and operational fit
Computer Science Engineering graduate with a CGPA of 8.95, specializing in Python, machine learning, and mobile application development. Experienced in building end-to-end ML pipelines using XGBoost and LSTM, interactive data dashboards. Proficient in data engineering, object-oriented programming, SQL, and agile development. Strong communicator and collaborative team player with a proven ability to deliver impact-driven software solutions.
Jyothi Engineering College, Cheruthuruthy
Bachelor of Technology · Computer Science and Engineering
August 1, 2022 – June 30, 2026
AI-Powered Wildfire Prediction System
August 1, 2025 – January 1, 2026
Cleaned and engineered environmental datasets including temperature, humidity, wind speed, and vegetation indices to build reliable machine learning inputs for wildfire risk modeling. Designed and trained an ensemble machine learning model combining XGBoost and LSTM with a severity classification layer, enabling accurate temporal wildfire risk forecasting 2 days in advance. Built an interactive map-based dashboard delivering real-time high-risk zone predictions and automating alert notifications to authorities, supporting proactive disaster prevention.
Femora – Women-Centric Mobile Application
January 1, 2025 – March 1, 2025
Developed a cross-platform mobile application using Flutter and Supabase with features including period tracking, SOS safety alerts, fitness video modules, nutrition guidance, and a community forum. Implemented secure backend services using Supabase for user authentication, real-time communication, and scalable data storage, supporting personalized health tracking and emergency safety alerts. Designed a modular UI/UX architecture in Flutter ensuring lightweight deployment, long-term maintainability, and seamless user experience across diverse connectivity environments.
Cultural Fit Analysis
The candidate's projects demonstrate a diverse interest in applying technology to real-world problems, from environmental prediction to women's health. This breadth, combined with an academic background in Computer Science and Engineering, suggests an individual who is curious, impact-driven, and capable of adapting to various project domains. The mention of 'Women in Technology' as an interest also indicates alignment with inclusive and diverse work cultures. The target role of 'AI Engineer' aligns well with the candidate's demonstrated skills in ML/AI and data engineering.
Soft Skills & Operational Fit
The candidate's resume highlights strong soft skills such as technical communication, team collaboration, critical thinking, and problem-solving. The project descriptions indicate an ability to work on complex problems and deliver functional solutions. The academic background and project diversity suggest a proactive and adaptable individual, suitable for collaborative and innovative environments.