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AI Engineer with less than a year in Machine Learning and Deep Learning with expertise in Python and
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Assessing your cultural and operational fit
Dedicated final-year CS graduate with hands-on experience in Python, Machine learning, Deep Learning, NLP, and BERT-based systems. Build end-to-end ML pipeline achieving accuracy 94.17% accuracy on medical EEG classification and 97.12% on BERT-based NER. Skilled in model evaluation, data preprocessing and feature engineering. Eager to grow, learn and contribute my analytical and technical skills in real world business problems.
YESHWANTRAO CHAVAN COLLEGE OF ENGINEERING
Bachelor of Technology · Computer Science and Engineering
November 1, 2022 – June 1, 2026
EEGNet
June 26, 2026 – Present
Developed a deep learning-based diagnostic system to optimize the ASZED dataset (153 EEG recordings: 76 patients, 77 controls) for early schizophrenia detection. Preprocessed (ICA + wavelet denoising) the EEG data and extracted multi-band features (Delta–Gamma) for improving model accuracy and automating manual effort by 15%. Designed CNN, LSTM, and hybrid model achieving 94.15%, 93.66% and 94.17% accuracy respectively.
Aviation Fuel Consumption Prediction
June 26, 2026 – Present
Built regression pipeline to predict fuel consumption, enabling cost optimization insights for aviation operations and presented insights via an interactive Streamlit application.
SmartDateExtract
June 26, 2026 – Present
Engineered a BERT-based NER deep learning model achieving 97.2% accuracy for expiry date detection across different diverse formats. Used a custom dataset of 500+ annotated food label images and designed a hybrid pipeline (regex + BERT) supporting 15+ date formats, eliminating false positives (0%). Built a real-time expiry alert app for consumer usage for reducing manual error and boosting user engagement.
AWS Machine Learning Foundations – Digital Badge
Unknown
June 1, 2026 – Present
Certification Completion - The Complete Python Bootcamp From Zero to Hero in Python
Unknown
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a strong interest in AI/ML applications across diverse domains (medical diagnostics, computer vision/NLP for product labels, aviation analytics). This diversity, coupled with a clear focus on AI engineering, aligns well with an AI Engineer role. The candidate is a final-year student, indicating a strong learning curve and eagerness to contribute, which is a positive cultural fit for growth-oriented teams. However, the lack of professional experience means less exposure to corporate culture and team dynamics.
Soft Skills & Operational Fit
The candidate's involvement in a club as a 'Core Team Member' suggests some organizational and promotional skills. However, without specific psychometric or English test results, it is difficult to assess communication clarity, work attitude, stress handling, or team collaboration in an operational context. The project descriptions are clear and concise, indicating good technical communication.