# Ingride N.

> Software Engineer | Purdue Graduate Student

Location: New York, New York, United States
Profile: https://flows.cv/ingride

My professional work experience at Sony Music Entertainment, Vanta, Slack Technologies, and Alliance Data, as well as the undergraduate research I conducted through my participation in the UMD CS Honors cohort have broadened my understanding of the field of artificial intelligence. It is important to not only use computer science, but to understand how these core principles are adapted to artificial intelligence with appropriate risk management, governance, compliance.

## Work Experience
### Software Engineer @ Sony Music Entertainment
Jan 2022 – Present | New York, United States
🔷 Technical Compliance - Designing and building scalable music distribution solutions with a modern AWS-based architecture and AI integration. Improving compliance with Apple Music and many other DSP's globally! 🔹

### Software Engineer @ Vanta
Jan 2022 – Jan 2022 | Washington DC-Baltimore Area
Enterprise Trust Management - Audit Enablement Team

### Software Engineer @ Slack
Jan 2020 – Jan 2021 | Washington DC-Baltimore Area
Service Engineering & SRE

### Student Leader/ Co-Founder @ Rewriting the Code
Jan 2019 – Jan 2020 | San Francisco Bay Area
➢ Student leader of the inaugural Black Wings division of Rewriting the Code. This is a global collegiate organization for women in information technology. My relationship with the program began when I was contacted by the organization's president during my stay in the Bay Area in June 2019, and was invited to lunch with other college professionals that summer. Received a $5000 scholarship for my involvement in partnership with the Lyft HBCU Recruiting program.

### Software Engineering Intern @ Slack
Jan 2019 – Jan 2019 | San Francisco Bay Area
Service Engineering

### Research Assistant - Machine Learning @ University of Maryland
Jan 2019 – Jan 2019
Center for Bioinformatics and Computational Biology

➢ Performed data imputation on genetic interaction (GI) networks. Used 80% training data and 20% test data on a genetic interaction network of >700 gene pairs to train a kernelized probabilistic matrix factorization (KPMF) model. 

➢ Developed a commute-time (CT) kernel based on protein-protein interaction (PPI) networks to function as a metric of similarity for the KPMF model.


## Education
### Master of Science - MS in Artificial Intelligence Management and Policy
Purdue University
Jan 2025

### Bachelor of Science - BS in Computer Science
University of Maryland
Jan 2016 – Jan 2020


## Contact & Social
- LinkedIn: https://linkedin.com/in/ingride-ngaku

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Source: https://flows.cv/ingride
JSON Resume: https://flows.cv/ingride/resume.json
Last updated: 2026-03-23