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ML Engineer with less than a year in Python & TensorFlow
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Hackathon-winning Machine Learning Engineer and final-year B.Tech CSE student (CGPA 8.08/10) with hands-on experience building and deploying end-to-end ML pipelines. Developed a Chronic Disease Prediction System achieving 94%+ accuracy and a real-time Facial Attendance System with 98%+ recognition accuracy using CNN and OpenCV. Proficient in Python, TensorFlow, Scikit-learn, NLP, LLMS, RAG, and Generative AI. Seeking an ML Engineer role to deliver measurable, data-driven impact.
GIET University
B.Tech · Computer Science & Engineering
August 1, 2022 – June 30, 2026
CTTC
Machine Learning Engineer Intern
May 1, 2025 – June 1, 2025
Bhubaneshwar, Odisha, India
Chronic Disease Prediction System
June 24, 2026 – Present
Developed ML models (Random Forest, XGBoost) to predict chronic diseases (diabetes, heart disease) on 10,000+ clinical records, achieving 94%+ accuracy with 12% F1-score improvement via SMOTE and GridSearchCV. Built an interactive Streamlit dashboard for real-time patient risk prediction, reducing manual clinical assessment effort by ~50%. Performed end-to-end EDA, feature engineering, class imbalance handling, and model evaluation using cross-validation and ROC-AUC metrics.
Facial Attendance System
June 24, 2026 – Present
Built a real-time automated attendance system using facial recognition (OpenCV + CNN embeddings) with 98%+ identification accuracy across 50+ registered users, eliminating manual attendance entry. Implemented deep learning face encoding pipeline; integrated SQLite for auto-logging attendance with timestamps and generating daily CSV reports. Added anti-spoofing measures and optimized for low-light and partial occlusion scenarios, improving classroom reliability by 30%.
Data Structures & Algorithms Using Java
NPTEL
June 1, 2026 – Present
AI/ML Practical Training
CTTC, Bhubaneswar
January 1, 2025 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying ML to practical problems, aligning well with an ML Engineer role focused on delivering tangible impact. The diversity of projects (healthcare, attendance systems) and exposure to various ML/AI sub-fields (CV, NLP, GenAI) suggest adaptability and a continuous learning mindset. Participation in hackathons further indicates a proactive and competitive spirit, which can be a good cultural fit for dynamic tech environments. However, the experience level is very junior, which might require significant mentorship.
Soft Skills & Operational Fit
The candidate's project descriptions highlight problem-solving, attention to detail (e.g., anti-spoofing, low-light optimization), and a results-oriented approach (e.g., reducing manual effort, improving accuracy). Collaboration with senior engineers during the internship suggests an ability to work in a team. The hackathon achievements indicate initiative and ability to deliver under pressure.