AI Engineer with 1+ years in Data Analysis and Machine Learning.
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Data-driven professional with a Master's in Data Science and a Bachelor's in Artificial Intelligence and Machine Learning Engineering. Specializes in building machine learning pipelines, developing computer vision systems, and implementing NLP solutions. Experienced in Python, SQL, and TensorFlow, leveraging advanced analytics and deep learning frameworks to tackle real-world challenges while prioritizing ethical AI and data privacy.
Jawaharlal Nehru Technological University - HYDERABAD
Master's Degree · Data Science
August 1, 2024 – June 30, 2026
Jawaharlal Nehru Technological University - HYDERABAD
Bachelor's Degree · Artificial Intelligence and Machine Learning Engineering
August 1, 2020 – June 30, 2024
NA
Intern
June 1, 2025 – Present
India
Deep SleepNet: Real-Time Driver Drowsiness Detection System
March 1, 2025 – August 1, 2025
Engineered a real-time computer vision system using OpenCV and Dlib to detect driver drowsiness through facial landmark analysis from live video streams, achieving 94%+ accuracy across diverse driving conditions. Implemented deep learning pipeline using TensorFlow and ResNet-based ImageNet transfer learning for robust facial feature extraction and behavior classification. Optimized model performance through rigorous evaluation using accuracy, precision, recall, and F1-score metrics; implemented non-intrusive alert mechanism using Pygame for real-time driver notifications. Addressed ethical considerations and privacy concerns in system design for practical deployment in intelligent transportation systems. Published full paper accepted to IEEE ICEC2NT 2026 conference on AI-based driver safety applications.
Secured Online Monitoring System for GIS Against Cyber Attacks Using IoT & Machine Learning
October 1, 2023 – June 1, 2024
Architected and deployed an intelligent IoT-based monitoring system to detect and prevent cyber attacks on Geographic Information Systems (GIS) infrastructure. Role: Team Leader. Engineered real-time data collection and analysis pipeline processing IoT sensor data and network traffic logs to identify anomalies and intrusion patterns with minimal false positives. Trained and evaluated supervised machine learning models (SVM, Random Forest) achieving high classification accuracy for threat detection and attack vector prediction. Performed comprehensive feature engineering on IoT communication logs including data preprocessing, normalization, and feature selection techniques to enhance model discriminative power. Validated model performance using confusion matrix and key metrics (accuracy, precision, recall) ensuring robust cyber threat identification. Integrated automated, real-time detection mechanisms into IoT ecosystem, enabling proactive security interventions through AI-based threat intelligence.
Fake Online Reviews Detection Using Machine Learning & Natural Language Processing
February 1, 2023 – August 1, 2023
Developed end-to-end NLP pipeline to identify deceptive online reviews, achieving 92% classification accuracy using optimized feature engineering and machine learning algorithms. Preprocessed text data through tokenization, stop word removal, lemmatization, and TF-IDF vectorization to enhance model performance on labeled review datasets. Trained and evaluated classification models (Logistic Regression, SVM, Naive Bayes) utilizing feature engineering that emphasized sentiment polarity, review length, frequency patterns, and syntactic structures for improved accuracy. Validated model performance using confusion matrix and ROC-AUC analysis to reduce false positives and optimize decision thresholds, ensuring reliable outputs. Demonstrated real-world applicability for e-commerce and service platforms, enhancing trust and decision-making through AI-powered review analysis.
Python for Data Science and AI
Unknown
June 1, 2026 – Present
Google Advanced Data Analytics
June 1, 2026 – Present
Google Gemini Certificate
June 1, 2026 – Present
Databricks Accredited Generative AI Fundamentals
Databricks
June 1, 2026 – Present
LangChain Academy Course
LangChain Academy
June 1, 2026 – Present
AWS & JP Morgan Chase Forage Virtual Internship
JP Morgan Chase
June 1, 2026 – Present
BCG Forage Virtual Internship
BCG
June 1, 2026 – Present
Nestle Internship Program
Nestle
June 1, 2026 – Present
Microsoft Office Certification
Microsoft
June 1, 2026 – Present
One Road Map Data Analyst Certificate
One Road Map
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in applying AI/ML to diverse real-world problems, including driver safety, cybersecurity, and e-commerce. This breadth of application, coupled with a focus on ethical AI and data privacy, suggests a good cultural fit for roles that value innovation, problem-solving, and responsible AI development. The 'Team Leader' role in one project also indicates leadership potential and collaborative spirit. The numerous certifications, including those from Google, Databricks, and LangChain, show a proactive approach to continuous learning and staying current with industry trends.
Soft Skills & Operational Fit
The candidate highlights 'Quick learner', 'Fast learner', and 'Teamwork and collaboration' as soft skills. Project descriptions indicate an ability to work in a team (Team Leader role in one project) and a focus on real-world applicability and ethical considerations, which are positive indicators for operational fit. However, the internship description is generic and does not provide specific examples of these soft skills in a professional setting.