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Yogesh Naresh

Professional Summary

US citizen and Computer Engineering student at Binghamton University, pursuing a B.S. in Computer Engineering (May 2026), GPA 3.80. Experienced in Python, Java, C, ML, and cloud technologies, with hands-on work in embedded firmware, FPGA-based design, and full-stack development. Former AI Software Developer Intern at Siemens Digital Industries and Teaching Assistant in the Digital Design Lab, with strong collaboration and problem-solving skills.

Professional Experience

AI Software Developer Intern

Siemens Digital Industries

Jul 2025—Aug 2025

•Fine-tuned a domain-specific LLM using PEFT (LORA) to convert natural language queries into structured JSON for backend data retrieval APIs, enabling structured search over PLM data.

•Optimized training using quantization, LoRA and FSDP, reducing GPU memory requirements by ~50% and enabling development on lower-cost EC2 instances.

•Built and deployed a serverless interface pipeline using AWS SageMaker, Lambda and API Gateway to process real-time queries and return structured outputs for downstream systems.

Teaching Assistant - Digital Design Lab

Binghamton University

May 2025—Present

•Supported a class of 50+ students in an FPGA-based design lab using ARTIX-7, assisting with debugging, hardware integration, and design concepts

•Evaluated and graded assignments and lab submissions, ensuring correctness and adherence to design specification and coding standards

•Guided students through implementation challenges in digital systems, improving understandings of FPGA workflows and hardware-software interaction

Education

B.S Computer Engineering

Binghamton University, Watson College Of Engineering | Vestal, NY

2026-01

•GPA: 3.80

•Dean's List (Fall 2024, Spring 2024, Fall 2025)

•Relevant Coursework: Machine Learning, Python Programming, Cloud Computing, C Programming, Object Oriented Programming, Computer Architecture, Operating Systems, Database Management Systems, Digital Systems Design

Skills

Python

Expert

Java

Expert

VHDL

Advanced

SQL

Expert

ReactJS

Advanced

TypeScript

Advanced

ElectronJS

Advanced

ExpressJS

Advanced

AWS SageMaker

Advanced

AWS S3

Advanced

AWS EC2

Advanced

Git

Expert

Docker

Advanced

Kubernetes

Advanced

MongoDB

Advanced

AMD Vivado

Advanced

Projects

Capstone Project (Battery Management System) Software Lead

•Led development of a battery management system, collaborating with electrical engineers to design a data pipeline collecting real-time signals from 12 voltage sensors, 1 current sensor, and 4 temperature sensors

•Developed firmware in Embedded C (Arduino) to sample analog signals, perform ADC conversion, format data into JSON, and stream readings via USB serial to a host system

•Built a desktop application with ElectronJS to parse incoming serial data, compute battery health metrics (state of health, voltage, temperature) and visualize results with SQLits used for persistent storage and historical analysis

Embedded C (Arduino)JSONUSB serialElectronJSSQLites

Machine Learning Algorithms from Scratch | Python (Machine Learning, Numpy)

•Implemented core ML models from scratch including Perceptron, Linear/Logistic Regression, SVM, k-NN, and Multilayer Perceptron, and trained them on the MNIST handwritten digits dataset

•Developed learning algorithms (Gradient Decent, Loss Minimization), with regularization techniques, and optimizing hyperparameters using cross-validation to improve model generalization

•Evaluated model performance using In-sample and Out-of-sample Error, analyzing the models performance

PythonNumpy

Sports Team Manager | Full Stack (Flask, Javascript, MongoDB) Fall 2024

•Built a full-stack web application for a real client to manage teams, players, and communications, supporting separate coach and player portals

•Designed and implemented RESTful APIs, session-based authentication, and role-based access control to enable secure multi-user interactions

•Developed backend features for team management, messaging, and notifications, allowing coaches to coordinate across multiple teams and players

FlaskJavascriptMongoDB

Music Recommender | Machine Learning (Python) Fall 2023

•Built a recommendation system using decision trees and Scikit-learn to suggest music based on user features

•Processed and analyzed datasets using Pandas, applying data preprocessing and feature selection techniques

•Developed a Tkinter-based interface to enable interactive user input and display recommendations

PythonPandasScikit-learnTkinter

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