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AI Engineer with less than a year in Machine Learning & NLP
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Data Science postgraduate at SVKM's NMIMS Mumbai (CGPA 8.94/10) with hands-on experience in Machine Learning, Deep Learning, NLP, and Computer Vision. Proven ability to build end-to-end AI systems - from predictive analytics pipelines to production-grade multi-modal SaaS platforms - through internships and independent projects. Passionate about applying data-driven solutions to real-world problems across healthcare, business intelligence, and conversational AI.
Nilkamal School of Mathematics, Applied Statistics & Analytics, SVKM'S NMIMS, Mumbai
M.Sc. · Data Science
August 1, 2024 – June 30, 2026
School of Emerging Sciences & Technology, Gujarat University
B.Sc. · Data Science
August 1, 2020 – June 30, 2023
Mount Carmel High School
Senior Secondary - 12th
N/A – May 31, 2020
Mount Carmel High School
Senior Secondary - 10th
N/A – May 31, 2018
The Special Character
AI/ML Intern
January 1, 2026 – May 1, 2026
Gujarat, India
Biztic Technologies
Data Analyst Intern
July 1, 2023 – December 1, 2023
Gujarat, India
Brio – AI-Powered Booking Platform
January 1, 2026 – May 1, 2026
Designed and developed a full-stack, multi-modal AI booking SaaS platform enabling customers to make service appointments through natural language chat, voice, or a structured booking wizard. Implemented a hybrid NLU pipeline combining keyword-based intent detection with Sentence-BERT semantic embeddings, achieving 94.2% service identification accuracy, and engineered a ten-state Deterministic Finite Automaton for session management ensuring reliable, context-aware multi-turn conversation flows. Integrated Cal.com API v2 for real-time slot availability and booking execution, LiveKit and Cartesia Sonic-3 for multilingual voice interaction across 40+ languages, and PostgreSQL with Prisma ORM for multi-tenant data isolation. Built a React-based owner SaaS dashboard for booking management, analytics, and AI configuration, and validated the system with a 171-test automated suite achieving 96.5% functional correctness.
Mental Health AI Chatbot
January 1, 2025 – December 1, 2025
Built an AI-driven mental health support chatbot by designing and curating a multi-source labeled dataset and developing end-to-end NLP pipelines for sentiment analysis and mental health classification using transformer models like RoBERTa and BERT. Involved preprocessing large-scale social media data, training and evaluating machine learning models for emotion and crisis detection, and handling real-time streaming capabilities with backend services built in Python and Flask.
Lane Detection and Turn Prediction System
September 1, 2024 – October 1, 2024
Built a computer vision-based lane detection and turn prediction system using Python, OpenCV, and NumPy for autonomous driving applications. Applied Canny edge detection, Hough Transform, perspective transformation, and polynomial fitting to detect lanes and predict turns on various road types, demonstrating real-time accuracy and robustness under standard driving conditions.
Predicting the Delta Between Theatrical and OTT Releases
July 1, 2023 – December 1, 2023
Built an ML model to predict the gap between theatrical and OTT releases using features like platform, studio, genre, and budget. Applied advanced feature engineering techniques including normalisation and outlier filtering, improving model accuracy. Achieved optimal performance with Random Forest Regressor using K-Fold Cross Validation and deployed the model via API for real-time predictions.
Early Stroke Detection Through Predictive Analytics
January 1, 2023 – March 1, 2023
Developed a model to predict stroke risk using features like age, hypertension, glucose level, and smoking status. Preprocessed data by removing irrelevant columns, applying random oversampling to address class imbalance, and engineered relevant features. Evaluated multiple classifiers (Logistic Regression, KNN, SVM, Decision Tree, Random Forest) using F1-score, achieving strong accuracy for early stroke risk detection.
Probability Theory: Foundation for Data Science
University of Colorado Boulder
March 1, 2024 – Present
Supervised Machine Learning: Regression and Classification
DeepLearning.AI
November 1, 2022 – Present
What is Data Science?
IBM
July 1, 2021 – Present
Cultural Fit Analysis
The candidate's project portfolio is diverse, covering autonomous driving, mental health, entertainment analytics, and a multi-modal booking platform. This breadth of interest and application aligns well with an innovative and dynamic work environment. The focus on end-to-end system development and practical application of AI/ML techniques suggests a results-oriented approach. The target role of 'AI Engineer' is well-aligned with the candidate's demonstrated skills and project experience, particularly in NLP, deep learning, and system integration.
Soft Skills & Operational Fit
The candidate demonstrates strong initiative and project ownership through several personal projects, including a full-stack AI booking platform. Their ability to work on diverse AI applications (computer vision, NLP, predictive analytics) suggests adaptability and a broad problem-solving mindset. The detailed project descriptions indicate good communication of technical work. However, without specific psychometric or English test scores, a comprehensive assessment of soft skills and operational fit is limited.