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AI Engineer with less than a year in Machine Learning & Deep Learning
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Electronics and Instrumentation Engineering student with practical exposure to sensors, embedded systems, and automation. Demonstrated ability to design, integrate, and test real-time monitoring systems using Raspberry Pi, MATLAB, and machine learning techniques.
V.R. Siddhartha Engineering College, Vijayawada
B.Tech · Electronics and Instrumentation Engineering
August 1, 2022 – June 30, 2026
Sri Chaitanya Junior College
Intermediate (BIEAP) · Physics, Chemistry and Mathematics
June 1, 2020 – May 31, 2022
Non-Invasive Glucose Monitoring System Using Raspberry Pi 3 and Machine Learning Algorithms
January 1, 2023 – June 1, 2026
Designed a low-cost, portable glucose monitoring device using Raspberry Pi and the max30102 optical sensor. Implemented near-infrared (NIR) spectroscopy and PPG signal analysis with machine learning for accurate, non-invasive glucose estimation. Integrated additional features to monitor heart rate, temperature, and respiration. Developed an SMS alert system for real-time abnormal reading notifications, supporting continuous health tracking and wearable healthcare innovation.
Breast Cancer Detection from Histopathology Images Using Deep Learning with ResNet121
January 1, 2023 – June 1, 2026
Designed and developed an automated breast cancer detection system using the ResNet121 deep learning architecture for classifying histopathology images as benign or malignant. Applied image preprocessing, stain normalization, data augmentation, and transfer learning techniques to improve classification performance. Achieved approximately 85% accuracy and an AUC score above 0.90, enabling reliable computer-aided diagnosis and supporting pathologists in early cancer detection.
ECG Image Classification Using Machine Learning Models
SRM University–AP
June 1, 2025 – Present
Cyber Security
All India Council for Technical Education (AICTE)
February 26, 2025 – Present
Enhancing Lung Cancer Detection Using Pretrained Deep Neural Network Models
2025 IEEE International Conference on Advancement in Technology (ICONAT)
January 1, 2025 – Present
CLOUD COMPUTING NPTEL Certification
NPTEL
January 1, 2025 – Present
VLSI Design for Testability
BIST Technologies Pvt. Ltd.
December 20, 2024 – Present
Designing of Obstructed Blood Vessel for Timely Identification of Cardiovascular Disorders
Annual International Conference on Recent Trends in Healthcare Innovation (AICRTHI 24) at Vidyavardhaka College of Engineering, Bangalore
October 25, 2024 – Present
INTERNET OF THINGS NPTEL Certification
NPTEL
January 1, 2024 – Present
DATA SCIENCE FOR ENGINEERING NPTEL Certification
NPTEL
January 1, 2024 – Present
INDUSTRIAL SAFETY ENGINEERING NPTEL Certification
NPTEL
January 1, 2024 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying AI to solve real-world problems, particularly in the healthcare domain, which aligns well with impact-driven organizations. The breadth of certifications (IoT, Data Science, Cloud Computing, Cyber Security) indicates a proactive learning attitude and a willingness to explore diverse technical areas. However, the candidate is still an undergraduate with no professional work experience, which might require more mentorship and integration into a professional team culture compared to a candidate with prior industry experience. The academic nature of all projects suggests a learning-oriented approach, which can be a good fit for a team that values continuous development.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving skills, particularly in designing and implementing complex systems. Their role as 'Team Lead' suggests leadership potential and ability to manage project components. The focus on healthcare applications indicates a potential for impactful work and attention to detail. However, without specific psychometric or English test scores, it's difficult to assess communication clarity, logical reasoning, work attitude, stress handling, or team collaboration beyond what's inferred from project descriptions.