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AI Engineer with less than a year in Machine Learning & Deep Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Highly motivated and results-oriented AI/ML Intern with 0.5 years of experience in developing and deploying machine learning models for real-world projects. Proficient in Python, TensorFlow, Scikit-learn, Pandas, and NumPy across the complete ML lifecycle. Proven ability to perform data preprocessing, feature engineering, and EDA on large datasets. Eager to apply deep learning and natural language processing skills to enhance patient engagement and awareness through innovative AI solutions.
Charotar University of Science and Technology
B.Tech · Computer Engineering
August 1, 2022 – June 30, 2026
BAPS Swaminarayam Vidhyamandir
HSC · Science
June 1, 2020 – May 31, 2022
BAPS Swaminarayam Vidhyamandir
SSC
June 1, 2019 – May 31, 2020
Let's Enkindle
AI/ML Intern
February 1, 2026 – Present
Ahmedabad, Gujarat, India
INFOICONIC
Intern
May 1, 2024 – June 1, 2024
Rajkot, Gujarat, India
Tiles Crack Detection
June 1, 2026 – Present
Developed a deep learning-based system for automated detection of cracks in tiles using CNN architectures. Trained and evaluated model (MobileNetV2) to improve defect classification accuracy. Achieved high precision and recall in detecting surface-level and structural tile cracks.
View ProjectHealthcare Chatbot
June 1, 2026 – Present
Developed an intelligent multilingual chatbot using Python and RAG (Retrieval-Augmented Generation) for accurate disease prediction and lab report explanation. Integrated with Flask for seamless web deployment, enabling real-time interactions and easy accessibility. The chatbot understands user symptoms in multiple languages and provides AI-generated medical responses, enhancing patient engagement and awareness.
View ProjectOffline Navigation System
June 1, 2026 – Present
Developed ML-powered offline navigation app using smartphone sensors (Accelerometer, Gyroscope, Magnetometer). Trained LSTM model on 5302 sensor readings achieving 91.61% activity classification accuracy. Implemented Dead Reckoning algorithm for real-time position prediction without GPS or internet. Deployed TensorFlow Lite model on Android for on-device inference with zero latency. Built complete React Native app with offline maps, moving cursor, and turn-by-turn navigation.
Data Visualization With Power BI
Unknown
June 1, 2026 – Present
Import Export Training Certificate
Unknown
June 1, 2026 – Present
Database Management Systems
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity (offline navigation, crack detection, healthcare chatbot) demonstrates a broad interest in applying AI/ML across different domains. The 'AI/ML Intern' role at Let's Enkindle aligns well with an AI Engineer target role, showing proactive engagement in the field. The listed soft skills like teamwork and adaptability are positive indicators for cultural fit. The candidate is still pursuing a B.Tech degree, indicating a strong learning mindset and potential for growth within a dynamic environment.
Soft Skills & Operational Fit
The candidate lists communication, consistency, teamwork, leadership, adaptability, and quick learning as soft skills. The internship experience at Let's Enkindle highlights collaboration with senior engineers and applying industry-standard practices, suggesting a good operational fit for team-based development. The project descriptions are clear and concise, indicating good communication skills. However, without direct assessment, these are self-reported and require validation.