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Nigam Mehta

@nigammehta

Electronics engineering student building Verilog processor and SoC IP projects, with FPGA implementation and embedded systems experience.

India

Message

I built an 8-bit pipelined processor in Verilog with hazard detection and implemented it on FPGA using Xilinx Vivado. My projects also include an AMBA APB4 monitoring IP core and a Python tool that automates Yosys synthesis and netlist SVG generation.

During my Microchip Embedded Systems Developer Intern role with AICTE–EduSkills, I completed a PIC16 track covering embedded C, BLE app development, and Azure IoT sensor nodes. I’ve also analyzed data and built dashboards at Elevate Labs, and developed Android app modules at EduSkills Foundation.

Experience

Work history, roles, and key accomplishments

Microchip Embedded Systems Developer Intern

AICTE–EduSkills (with Microchip)

Jun 2026 - Aug 2026 (2 months)

Completed an 8-week PIC16 microcontroller track covering MPLAB X, embedded C, BLE app development, Azure IoT sensor nodes with motor control, and real-time data visualization.

PIC16 MPlab X Embedded C BLE Microsoft Azure IoT Real Time Data Visualization

VLSI Design & Semiconductor Engineering Intern

EduSkills Foundation

Apr 2026 - Jun 2026 (2 months)

Studied digital IC architectures, RTL design methodologies, ASIC verification flows, and EDA tooling.

Digital IC Design RTL Design ASIC Verification EDA Tools

Data Analyst Intern

Elevate Labs

Feb 2026 - Mar 2026 (1 month)

Cleaned and analyzed 10,000+ records using Python, Pandas, and Power BI, built 5 dashboards, and automated reporting, cutting manual effort by 30%.

Python Pandas Power BI Data Cleaning Data Analysis Dashboarding Automation

Android Developer Intern

EduSkills Foundation

Oct 2025 - Dec 2025 (2 months)

Developed 3 Android app modules in Java/Kotlin covering UI screens and core app logic.

Android Java Kotlin UI Design Application Logic

AI & Machine Learning Intern

EduSkills Foundation / Edunet Foundation

Jun 2025 - Sep 2025 (3 months)

Trained and evaluated ML classification models using scikit-learn on labeled datasets, achieving 88% accuracy across preprocessing, training, and evaluation stages.

Machine Learning scikit learn data preprocessing Model Training Model Evaluation