Help & Supportcareers@gravityer.com
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with less than a year in Deep Learning and Machine Learning.
Zufshan Naaz is an aspiring AI Engineer with a Bachelor of Engineering in AIML, demonstrating strong foundational skills in Python, Deep Learning, and Machine Learning. Her academic projects, such as TerraScan (an AI-based soil classification web app) and MineGuard (an IoT-based safety helmet), highlight her practical application of CNNs, FastAPI, TensorFlow, and embedded systems. She is proficient in various databases and development tools, and holds certifications in Python for Data Science and Deep Learning, indicating a solid commitment to building intelligent solutions.
BMSIT&M, Bengaluru
Bachelor of Engineering · AIML (Artificial Intelligence and Machine Learning)
August 1, 2020 – June 30, 2024
Mount Carmel PU College, Bengaluru
PCMB · Physics, Chemistry, Mathematics, Biology
June 1, 2018 – May 31, 2020
Vatican High School, Bengaluru
SSLC
June 1, 2017 – May 31, 2018
TerraScan
June 1, 2024 – June 1, 2026
TerraScan is an AI-based soil classification web application that predicts soil type from uploaded soil images using a trained CNN deep learning model. The system also uses geographic coordinates to detect the location and provides estimated soil parameters such as N, P, K, Organic Carbon, and pH along with fertilizer recommendations. Built using FastAPI, TensorFlow, and Python, the project integrates image processing, machine learning, and geolocation services to assist farmers in making informed soil management decisions. Reduces cost and saves manual inspection time.
TraySure Restaurant Management System
June 1, 2024 – June 1, 2026
Developed a web-based platform using PHP, MySQL, and HTML/CSS for restaurant inventory and waste management. Implemented features for ingredient tracking, expiry monitoring, daily usage, and automatic monthly/yearly summaries. Added login-based access, surplus food donation, and cost-control insights to improve efficiency and sustainability.
MineGuard - Smart Helmet for Miners
June 1, 2024 – June 1, 2026
Engineered an IoT-based safety helmet using Arduino Mega 2560 with sensors for gas, temperature, humidity, obstacle detection, and heart rate. Integrated GPS, ESP32, GSM 800, and Blynk app for real-time tracking, wireless alerts, and remote monitoring. Implemented audio warnings and efficient power management with DFPlayer Mini, 12V lithium battery, and LM2596 for reliable operation in mines.
Python for Data Science
NPTEL
June 1, 2026 – Present
Introduction to Generative AI by Google
June 1, 2026 – Present
Introduction to Deep Learning
NPTEL
June 1, 2026 – Present
Achieved a high score of 88%, indicating a solid grasp of fundamental Data Science and AI concepts, which aligns well with the target role.
Strengths
Limitations
A score of 40% suggests a limited understanding of practical Python development, including Django and data anonymization, which are crucial for robust application development.
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying AI/ML to real-world problems (agriculture, restaurant management, mining safety), indicating a proactive and impact-driven mindset. The diversity of projects (web apps, IoT) shows versatility. However, the lack of professional experience and the academic focus of all projects suggest a need for mentorship and adaptation to a corporate environment. The psychometric test score is a concern for cultural fit, as it might reflect challenges in areas like teamwork or adaptability.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted problems, suggesting good problem-solving and project management potential. The academic nature of projects implies a structured approach to tasks. However, the psychometric test score is below average, which might indicate areas for development in logical reasoning, work attitude, stress handling, or team collaboration. Further assessment would be needed to confirm operational fit.
Strengths
Limitations
A very low score of 19% indicates significant deficiencies in fundamental Python programming, data analysis, algorithms, and unit testing, which are critical for any AI/ML role.
Strengths
Limitations