# Rishikesh Solapure

> Software Engineer | React, JavaScript, Python, Java, AWS | Interested in building highly available and scalable products

Location: Mountain View, California, United States
Profile: https://flows.cv/rishikeshsolapure

My role as a software engineer encompasses developing robust backend systems and deploying AI-cloud services with a focus on security and scalability. I bring cutting-edge solutions to complex technical challenges.

Working with almost 10+ teams throughout my studies and in industry, I have to work with multiple languages like Python, Java, JavaScript, and Go; however, my to-go tech stack for creating full-stack highly scalable and robust applications is Python and Javascript, due to the wide range of libraries and top-of-the-line functionality it provides. My interest in learning new technologies with constantly innovative myself has always been my learning mantra. 

I also write technical blogs and contribute to open source in my free time.

## Work Experience
### Software Engineer @ Peppermint Robotics
Jan 2025 – Present | United States

### Software Engineer @ Credence Global Solutions
Jan 2024 – Jan 2025 | Mountain View, California, United States

### Software Engineer Intern @ ASANTe
Jan 2023 – Jan 2024 | Boulder, Colorado, United States
Developed an MVP prototype using Golang that secured funding of $100,000.

### Software Engineer Intern @ Solfir
Jan 2021 – Jan 2022 | India
Engineered a full-stack e-commerce website, supporting over 500 student developers with medium articles and tutorials on web development.

### Machine Learning Research Assistant @ Pune Institute of Computer Technology
Jan 2021 – Jan 2021 | Pune, Maharashtra, India
Came up with the universal approach that can increase the accuracy of image classification tasks irrelevant to the domain. 

To increase overall precision, I used an ensemble model that combined different image data pre-processing techniques like segmentation, masking, and feature engineering. Convolutional neural networks (CNN) were used in this study to extract features from images and feed them to k-nearest neighbor classifiers (KNNs). 

After conducting numerous rigorous experiments, we were able to improve the model's accuracy and precision by 2%, yielding a 99.90% model. Later this approach was also tested on different image classification domains like medical, etc. This approach was presented in the ICACIE21 conference receiving 3 peer-to-peer reviewers.

### Data Scientist Intern @ Omdena
Jan 2021 – Jan 2021


## Education
### Master of Science - MS in Computer Science(Machine Learning)
University of Colorado Boulder

### Bachelor of Engineering - BE in Computer Science, Honors in Data Science
Pune Institute of Computer Technology


## Contact & Social
- LinkedIn: https://linkedin.com/in/rishikesh-solapure
- Portfolio: https://portfolio-blue-seven-80.vercel.app/

---
Source: https://flows.cv/rishikeshsolapure
JSON Resume: https://flows.cv/rishikeshsolapure/resume.json
Last updated: 2026-04-10