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AI Engineer with less than a year in AI/ML & Data Science.
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Assessing your cultural and operational fit
AI/ML enthusiast and Computer Science student with 5 months of hands-on experience in Artificial Intelligence and Data Science. Proven ability to develop and optimize machine learning models for image classification and natural language processing, achieving notable accuracy and efficiency improvements. Skilled in Python, TensorFlow, Keras, and NLTK, with a strong foundation in MLOps, deep learning, and computer vision, as demonstrated through impactful projects and internships.
PES COLLEGE OF ENGINEERING
B.E. · Computer Science (AI & ML)
December 1, 2022 – May 1, 2026
ST. PHILOMENA'S PU COLLEGE
12th Grade
June 1, 2020 – May 1, 2022
Opportive
Data Science & Analytics Intern
July 1, 2025 – August 1, 2025
Mysore, Karnataka, India
Personifwy
Artificial Intelligence Intern
May 1, 2025 – July 1, 2025
Mysore, Karnataka, India
Smart Attendance System
June 10, 2026 – Present
Automated real-time student attendance using SSD-based face detection and 128D ResNet face embeddings, achieving 84% recognition accuracy with minimal human involvement. Constructed and fine-tuned a face recognition pipeline using enrolled image datasets, sustaining stable performance across varying lighting conditions and camera angles. Deployed a Flask-based dashboard that reduced manual attendance marking time by 70-80%, enabling real-time logging, CSV export, proxy prevention, and efficient system monitoring.
AI Resume Matcher
June 10, 2026 – Present
Improved job-resume matching accuracy by developing a machine-learning-based resume classification system using Python and scikit-learn to predict relevant job categories from resume text. Refined feature representation for predictions by applying NLP preprocessing and TF-IDF vectorization using NLTK and SpaCy, resulting in clean, structured input for the model. Built an end-to-end resume analysis application by integrating the trained model into a Flask web application with resume upload and result visualization.
DermAI
June 10, 2026 – Present
Developed a CNN-based classification system to identify skin diseases from labeled dermatological images, achieving 73% accuracy through structured preprocessing and model training. Integrated a location-aware healthcare support feature enabling users to discover nearby doctors, hospitals, and clinics via geolocation, facilitating faster preliminary screening and medical access.
Cultural Fit Analysis
The candidate's academic projects demonstrate a proactive approach to applying AI/ML concepts to real-world problems (attendance, resume matching, dermatology). Participation in and leadership of 'Prompt War' events indicate an interest in community engagement and fostering AI skills, which could contribute positively to a collaborative and innovative team culture. However, the experience is primarily academic and internship-based, which might require some adjustment to a full-time corporate environment.
Soft Skills & Operational Fit
The candidate's involvement in organizing and leading 'Prompt War 2.0' suggests leadership potential, event management skills, and an ability to design technical challenges. Collaboration in 'Prompt War' indicates teamwork and creative problem-solving under constraints. The project descriptions are clear, indicating good communication of technical work.