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AI ML Engineer with less than a year in Machine Learning & Computer Vision
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Assessing your cultural and operational fit
AI/ML enthusiast with a B.Tech in Computer Science with Honors in Artificial Intelligence and Machine Learning. Skilled in Python, machine learning, deep learning, and data analysis using libraries such as NumPy, Pandas, Scikit-learn, and TensorFlow. Experienced in building AI applications including image classification and predictive modeling with Streamlit.
IES, IPS Academy, Indore
B.Tech · Computer Science Engineering
August 1, 2021 – June 30, 2025
Kendriya Vidyalaya No.1, Sagar
XII CBSE
June 1, 2020 – May 31, 2021
Infograins Software Solutions Pvt. Ltd.
AI/ML Intern
September 1, 2025 – March 1, 2026
India
Driver Drowsiness Detection System
June 10, 2026 – Present
Developed a Computer Vision based driver fatigue detection system using deep learning to identify eye closure and yawning patterns. Implemented CNN models for eye state and yawn detection to monitor driver alertness in real time. Integrated OpenCV for real-time video processing and fatigue scoring with alarm alerts. Designed the system to improve road safety by detecting driver drowsiness and triggering alerts. Built an interactive user interface using Streamlit.
Cat vs Dog Image Classifier
June 10, 2026 – Present
Developed a Convolutional Neural Network (CNN) based image classification model to accurately distinguish between cat and dog images. Performed image preprocessing and data normalization to improve model training efficiency and prediction performance. Built an interactive user interface using Streamlit that allows users to upload images and obtain real-time predictions.
House Price Prediction System
June 10, 2026 – Present
Developed a machine learning regression model to predict house prices based on property features such as area, number of bedrooms, bathrooms, and number of stories. Performed data preprocessing and exploratory data analysis (EDA) using Pandas to understand data patterns and improve model performance. Trained and evaluated the model using Scikit-learn regression algorithms to generate accurate price predictions. Designed an interactive web interface using Streamlit that enables users to input housing parameters and receive real-time price predictions.
Cultural Fit Analysis
The candidate's projects show a strong interest in practical applications of AI/ML, aligning well with an 'AI ML Engineer' role. The diversity of projects (Computer Vision, Regression, Classification) indicates a broad foundational interest. The academic nature of most projects and the single internship suggest a learning-oriented individual. However, the lack of diverse team projects or open-source contributions limits the assessment of cultural fit in a collaborative, fast-paced industry environment.
Soft Skills & Operational Fit
The candidate demonstrates a proactive approach to learning and applying AI/ML concepts through academic projects and an internship. The focus on building interactive applications with Streamlit suggests an understanding of user interaction and deployment, which is a positive for operational fit. However, as an entry-level candidate, direct experience in complex operational environments or team collaboration beyond academic settings is not explicitly detailed.