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AI Engineer with less than a year in GenAI & Machine Learning
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AI/ML Intern with practical experience in developing RAG-based Text-to-SQL pipelines, LLM-powered address normalization, and AI automation for JSON-to-narrative conversion. Proficient in GenAI, LLM API integration, vector databases (Qdrant), and Python frameworks like FastAPI and TensorFlow. Developed and deployed plant disease detection and SMS spam detection systems, demonstrating strong skills in CNN architecture, NLP, and model evaluation.
SARDAR VALLABHBHAI PATEL INSTITUTE OF TECH.,VASAD 041
Bachelor of Engineering · Computer Engineering
September 18, 2022 – May 26, 2026
Navaera Worldwide
AI Engineer
January 1, 2026 – April 22, 2026
Vadodara, Gujarat, India
Plant Disease Detection System
May 1, 2025 – June 1, 2025
Built and deployed a CNN-based plant disease classification system using TensorFlow/Keras, achieving 96.5% validation accuracy across 38 classes on a 17K+ image dataset. Designed and optimized a custom CNN architecture (7.8M parameters) with regularization (Dropout) and efficient data pipelines, maintaining 99% training accuracy with low generalization gap. Performed rigorous model evaluation using confusion matrix and per-class precision/recall metrics, ensuring strong performance consistency across imbalanced classes and deployed the model via a Streamlit-based application.
SMS Spam Detector
December 1, 2024 – January 1, 2025
Built a custom Natural Language Processing (NLP) pipeline incorporating tokenization, stopword removal, and stemming using NLTK to clean and curate 5,500+ SMS records, removing 400+ duplicates and reducing dataset size to 5,169 high-quality samples. Executed comparative model evaluation across multiple Naive Bayes architectures and vectorization techniques (Count Vectorizer vs. TF-IDF), selecting optimal models based on precision-critical system requirements to achieve 95.7% accuracy and 100% precision. Deployed the inference model via a real-time Streamlit web application.
AI For Manufacturing
Intel
February 1, 2025 – July 1, 2025
AICTE AI Internship
AICTE
December 1, 2024 – January 1, 2025
Cultural Fit Analysis
The candidate's academic projects and internship experience show a strong focus on AI/ML, aligning well with an AI Engineer role. The diversity of projects, from computer vision to NLP and advanced GenAI applications, indicates a broad interest and adaptability. Their involvement in certifications and internships suggests a proactive learning attitude and a desire to stay current with industry trends, which are positive indicators for cultural fit in a dynamic technical environment.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills and an ability to apply theoretical knowledge to practical scenarios, as evidenced by their project descriptions. Their experience with Docker and FastAPI suggests an understanding of deployment and operational aspects of AI systems. The detailed descriptions of their work imply good communication of technical concepts, though without direct interaction, this is an inference.