Rutesh Kalne

About

B.E. student in Electronics and Telecommunication Engineering with hands-on project experience in embedded systems, sensor interfacing, PCB design, and machine learning. Completed a short internship in solar site engineering with exposure to installation, wiring, testing, and safety procedures.

Experience

• Site Engineer Intern
  Novsun Solar
  Jun 2025 – Jul 2025

  Assisted in on-site installation and commissioning of solar power systems. Worked with the site team on panel mounting, wiring, and electrical connections. Followed safety procedures and helped with testing, inspection, and site documentation.

Education

• 2023 YM
  Higher Secondary Certificate (HSC)

  Yashwant Mahavidyalaya

  Mar 2023

  Mar 2023
• 2021 MP
  Secondary School Certificate (SSC)

  Mahatma Phule High School

  Mar 2021

  Mar 2021
• 2023 YC
  B.E. in Electronics and Telecommunication Engineering

  Yeshwantrao Chavan College of Engineering

  Electronics and Telecommunication Engineering (ETE) · Aug 2023

  Electronics and Telecommunication Engineering (ETE) · Aug 2023

Skills

Tools / apps / platforms

Projects

• BCD to 7-Segment Display System
  Digital Electronics, PCB Design, KiCad/EasyEDA

  Designed a BCD to 7-segment display circuit using the 74LS47 decoder IC. Converted binary inputs into readable decimal digit outputs accurately. Designed and fabricated a PCB for compact circuit implementation. Integrated DIP switches, resistors, and a common anode display. Tested, debugged, and validated the complete digital display system.

• Smart Multi-Sensor Monitoring System
  STM32, Embedded C, OLED Display

  Developed a real-time monitoring system using STM32 Blue Pill and Embedded C. Interfaced flame, LDR, touch, and tilt sensors for environmental sensing. Displayed sensor status and readings on an OLED display. Implemented buzzer-based alerts for abnormal sensor conditions. Tested and debugged hardware interfaces for reliable system operation.

• Stock Market Prediction System
  Python, Machine Learning, LSTM, Neural Network

  Built a system to predict stock market trends using historical stock data and machine learning techniques. Collected and cleaned price data (open, close, volume) to prepare it for analysis. Applied Linear Regression, LSTM, and Decision Tree models to estimate future stock prices. Used graphs and visual tools to identify patterns and improve prediction accuracy. Worked with recent market data to keep predictions practical and up to date.