Python Engineer with less than a year in Backend Development & Machine Learning
AI is analyzing your overall score…
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Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Computer Science graduate with hands-on experience building machine learning pipelines, data preprocessing workflows, and Python-based backend systems. Demonstrated ability to engineer features from raw data, train and evaluate classification models using Scikit-learn, and deploy production applications. Seeking to apply AI and data science skills to real-world engineering challenges.
Jain College of Engineering and Technology
Bachelor of Engineering · Computer Science
August 1, 2022 – June 30, 2026
DR R B Patil Mahesh PU College
Pre-University (PU 2)
June 1, 2020 – May 31, 2022
Shanti Niketan English Medium School
SSLC
June 1, 2008 – May 31, 2020
Aultum Inc.
Backend Development Intern
May 1, 2026 – Present
Hubli, Karnataka, India
Network Traffic Classification System using Machine Learning
June 16, 2026 – Present
Built a Django-based data pipeline to capture and analyze 100-300 live network packets per minute; conducted data preprocessing and feature engineering to extract 24 distinct network flow features for model training. Trained and evaluated a Random Forest multi-class classification model to categorize 11 traffic types, successfully isolating multiple DoS variants from benign traffic across 2 Kaggle datasets. Implemented protocol-level filtering for TCP, UDP, and ICMP to improve dataset quality and downstream model accuracy.
QR Code-Based Attendance Tracking System
June 16, 2026 – Present
Developed a web-based attendance system using dynamic QR code generation with automated session expiration to eliminate duplicate entries and spoofing. Designed a relational SQLite schema to store attendance logs and credentials, and implemented role-based authentication for separate student and faculty access controls. Added server-side session validation ensuring tamper-proof attendance records across all user interactions.
Cultural Fit Analysis
The candidate's academic projects demonstrate initiative and a willingness to tackle diverse technical challenges, from machine learning to web development. The internship at Aultum Inc. shows an ability to adapt to new technologies (migrating from Java Spring Boot to Python FastAPI) and contribute to critical system components like authentication and RBAC. This adaptability and breadth of technical interest suggest a good cultural fit for a dynamic engineering environment. The focus on secure systems and clear documentation also aligns with best practices in many engineering cultures.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a structured approach to problem-solving and an understanding of system architecture. The internship experience suggests an ability to work on enterprise-level projects and document systems for cross-team clarity, which are positive indicators for operational fit. However, without direct assessment data, further evaluation of soft skills like teamwork, leadership, and adaptability is limited.