Nachiket Kolte

Pune

Summary

Detail-oriented software developer with a focus on delivering robust, high-quality solutions. I take the time to fully understand project requirements before implementation, ensuring consistent and reliable outcomes. Committed to following best practices and industry standards, I'm always ready to work hard and adapt to achieve the desired results. A strong advocate for clean code, I strive for continuous improvement in every project.

Overview

years of professional experience

Work History

Software Developer (PyTeF Developer)

Lenze SDC

Pune

08.2021 - Current

Developed a comprehensive software solution for device testing in the End of Line process using FastAPI.
Created and supported PyTeF APIs for device interaction.
Automated frontend and backend operations for system integrity checks.
Ensured clean, maintainable code following best OOP practices.
Designed UML diagrams (sequence, class) and maintained project documentation.

Education

B. E - E&TC

Modern Education Society's College of Engineering, Pune, India

05-2021

Skills

Python
OOPs

Fast API
HTML, JavaScript, CSS

Timeline

Software Developer (PyTeF Developer) - Lenze SDC

08.2021 - Current

Modern Education Society's College of Engineering - B. E, E&TC

PROJECTS

End of Line Software Developement: Developed a comprehensive application for automating device validation in multiple international manufacturing plants using Python, FastAPI, and Websockets.
Automation of OPC UA Communication Verification: Created an automation program to fully verify OPC UA communication across various controllers and devices.
Automation of Cybersecurity Compliance Verification: Developed a solution to assess cybersecurity aspects of controllers and devices, ensuring compliance with the latest European Union regulations.
Codesys PLC API Library: Developed a complete, user-friendly API library in Python for accessing all features of the Codesys PLC designer tool, integrating backend and frontend functionality.
Handwritten Devanagari Character Recognition: Built a deep learning model using ResNet to accurately recognize handwritten Devanagari characters, optimizing for high accuracy.