cv
Basics
| Name | Gowtham Arulmozhi |
| gowtham.arulmozhii@gmail.com | |
| Phone | (584) 286-2416 |
| Url | https://wothmag07.github.io/ |
| Summary | AI Engineer with a strong foundation in MLOps, cloud infrastructure, and scalable system design. Experienced in deploying real-time AI applications and automating end-to-end ML workflows in production. |
Work
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2025.03 - 2025.06 Graduate Teaching Assistant
Oregon State University
Serving as a Graduate Teaching Assistant for an online capstone course, guiding students across AI, DevOps, Full Stack, and Database projects. Provided constructive feedback, assessed deliverables, and collaborated with instructors to enhance the learning experience and facilitate effective team coordination.
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2024.03 - 2024.12 Research Assistant
Oregon State University
Built automation tools leveraging large language models to align healthcare data schemas and developed Python-based utilities to efficiently handle BibTeX and SQL DDL files, simplifying data integration and database setup processes.
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2021.08 - 2023.09 Associate Technical Architect
National Payments Corporation of India
My responsibilities revolved around designing and managing scalable cloud infrastructure, implementing CI/CD pipelines, and enhancing system observability. I worked on end-to-end machine learning workflows, from model development to deployment using tools like Kubeflow, and contributed to real-time AI solutions such as fraud detection and number plate recognition.
Education
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2023.09 - 2025.12 Corvallis, OR, USA
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2017.07 - 2021.05 Chennai, TN, India
Bachelor of Engineering
SSN College of Engineering, Anna University
Electronics and Communication Engineering
Certificates
| Certified Kubernetes Administrator | ||
| Linux Foundation | 2025-05 |
| AWS Solutions Architect - Associate | ||
| Amazon Web Services(AWS) | 2024-10 |
| AWS Certified Cloud Practitioner | ||
| Amazon Web Services(AWS) | 2024-07 |
Publications
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2020.11 Detection of Arrhythmia using ECG waves with Deep Convolutional Neural Networks
Gowtham Arulmozhi
This work explores the use of deep convolutional neural networks (CNNs) to automatically detect arrhythmias from ECG waveforms, aiming to improve early diagnosis by learning complex patterns in cardiac signals. The approach leverages deep learning to classify heart rhythm irregularities with minimal feature engineering.