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Python Engineer with less than a year in Backend Development & Cloud Computing
Information Technology postgraduate (ME, VGEC Ahmedabad) with strong expertise in backend development, cloud computing, machine learning, and distributed systems. Proficient in building scalable and production-grade applications using Python, Django, REST APIs, Docker, and microservices architecture. Experienced in machine learning-driven systems including time-series forecasting (LSTM) and cloud resource optimization. Author of two research papers submitted to ICACS 2026 in cloud resource forecasting and intelligent autoscaling. Strong foundation in software engineering principles, data structures, algorithms, and system design with a focus on performance, scalability, and reliability. Seeking Research Engineer, Machine Learning Engineer, and AI-driven Systems roles with a focus on scalable machine learning, cloud computing, and impactful research applications.
Vishwakarma Government Engineering College (VGEC)
Master of Engineering · Information Technology
August 1, 2024 – June 30, 2026
Government Engineering College (GEC)
Bachelor of Engineering · Computer Engineering
August 1, 2020 – June 30, 2024
P. R. Mukhi Secondary and Higher Secondary School
Higher Secondary Certificate (HSC)
N/A – May 31, 2020
Shree C. N. Vidhyalaya
Secondary School Certificate (SSC)
N/A – May 31, 2018
BrainyBeam Info-Tech Pvt. Ltd.
Python Developer Intern
January 1, 2024 – April 1, 2024
Ahmedabad, Gujarat, India
E-Commerce Web Application
January 1, 2024 – April 1, 2024
Developed a full-stack e-commerce platform with secure user authentication, cart management, and responsive UI. Integrated REST APIs using Django for product management and order processing workflows. Implemented Razorpay payment gateway, enabling secure and seamless transactions. Improved user experience through optimized frontend design and efficient backend data handling.
Advanced Billing System with QR Code
January 1, 2024 – April 1, 2024
Built an automated billing system to manage customer records, invoices, and payments efficiently. Implemented QR-based billing and real-time data updates, improving operational speed and accuracy. Designed scalable architecture with secure payment integration and reporting capabilities. Enhanced accessibility with mobile-friendly interface and streamlined user workflows.
Cloud Resource Utilization Forecasting
January 1, 2024 – June 1, 2026
Compared ARIMA, SARIMA, Holt-Winters, Exponential Smoothing, and LSTM models for CPU workload prediction. Achieved highest accuracy using LSTM for complex cloud workload forecasting. Evaluated models using MAE, RMSE, and MAPE metrics to improve prediction reliability. Contributed to improved cloud resource utilization and system efficiency.
Autoscaling in Cloud Environments
January 1, 2024 – June 1, 2026
Developed predictive autoscaling system using LSTM for intelligent resource allocation. Integrated Prometheus for monitoring and Locust for load testing under dynamic workloads. Reduced response time and improved resource efficiency in cloud environments. Implemented both proactive and reactive scaling strategies for better system performance.
Machine Learning & Time-Series Forecasting (LSTM) – Academic & project-based experience
Unknown
June 1, 2026 – Present
Docker & Microservices – Hands-on experience with containerized application development and deployment
Unknown
June 1, 2026 – Present
Comparative Analysis of Statistical and Deep Learning Approaches for Cloud Resource Utilization Forecasting
ICACS 2026 2nd International Conference on Artificial Intelligence, Communication Technologies & Smart Cities
January 1, 2026 – Present
Efficient Resource Management Through Proactive and Reactive Autoscaling in Cloud Environments
ICACS 2026 2nd International Conference on Artificial Intelligence, Communication Technologies & Smart Cities
January 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic projects demonstrate a proactive approach to learning and applying advanced concepts like machine learning and cloud autoscaling. The research publications indicate a drive for innovation and contribution to the technical community. The internship experience shows an ability to work in a professional, team-based environment. However, the focus is heavily academic, and there is limited evidence of diverse industry experience or contributions outside of academic projects, which might impact cultural fit in a fast-paced, product-driven commercial setting.
Soft Skills & Operational Fit
The candidate's resume highlights collaboration in an Agile environment and clean coding practices, suggesting a good operational fit. The project descriptions indicate an ability to work on full-stack applications and improve system performance. However, without direct assessment data on soft skills, a comprehensive evaluation of communication, teamwork, and problem-solving under pressure is limited.