Renuka Nale

Software Engineer

Rochester, New York, United States1 yr 10 mos experience

Key Highlights

Developed intelligent assistants using RAG and LangGraph.
Created scalable web applications with Java and React.
Designed computer vision systems leveraging TensorFlow and OpenCV.

Stackforce AI infers this person is a Software Engineer specializing in AI/ML and full-stack development.

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Skills

Core Skills

Python

Other Skills

Retrieval-Augmented Generation (RAG)KerasStreamlitOpenCVGitTensorFlowANSALangChainLangGraphVector DatabasesComputer VisionCUDAcore javacMachine learning

About

I am a versatile Software Engineer with hands-on experience in AI/ML, full-stack development, and data-driven solutions. I have built intelligent assistants using RAG and LangGraph, developed scalable web applications with Java, Spring Boot, and React, and designed computer vision systems leveraging Python, TensorFlow, and OpenCV. I thrive on solving complex problems and delivering innovative, efficient solutions.

Experience

1 yr 10 mos

Total Experience

11 mos

Average Tenure

Current Experience

Paychex

Software Engineer Intern

Jul 2025 – Present · 10 mos · New York, United States · Remote

PythonRetrieval-Augmented Generation (RAG)

Binghamton university

Software Engineer

Jul 2024 – Jun 2025 · 11 mos · New York, New York, United States · Remote

1- Developed a real-time face mask detection application using Python, Keras, and Streamlit.
2- Implemented OpenCV Haar Cascades for face detection within live video streams.
3- Applied transfer learning with MobileNet to accurately classify mask vs. no-mask cases.

PythonKeras

T-systems solutions for research gmbh

Machine Learning Researcher

Mar 2017 – Feb 2018 · 11 mos · Stuttgart, Baden-Württemberg, Germany · On-site

Title: Use of machine learning for feature detection in CAD meshes.
1- Automated image preprocessing and dataset generation for CAD models using Python scripts and ANSA.
2- Created a labeled CAD image dataset with the labelImg tool, forming the foundation for supervised learning models.
3- Extended the TensorFlow Object Detection API to detect CAD features, automating the defeaturing step in the CAE workflow.
4- Evaluated multiple pretrained models on the generated dataset, optimizing for accuracy and efficiency.

PythonGit