Etsuji Nakai
AI Specialist Customer Engineer, Google Cloud
Tokyo, Japan
Summary
👤
Senior🎓
Top SchoolEtsuji Nakai is an AI Specialist Customer Engineer at Google Cloud with 11 years of experience designing cloud infrastructure and machine learning solutions, blending hands-on MLOps work with customer-facing architecture and technical marketing. Based in Tokyo, he has progressed through solutions architect roles at Google after a background in open source and Linux advocacy at Red Hat and IBM, and teaches ML and cloud courses at WASEDA NEO and NII. His open-source contributions include ML training labs and MLOps examples for GoogleCloudPlatform and quantum ML notebooks, demonstrating practical model training, serving on AI Platform, and implementations of QAOA. Comfortable across system architecture, TensorFlow/Keras deployments, and performance monitoring, he often bridges research-grade ideas (quantum ML) with production-ready pipelines. Notably, he has a physics master's from Kyoto University and a longstanding interest in education technology dating back to early-career teaching roles.
12 years of coding experience
17 years of employment as a software developer
Master, Physics, Master, Physics at Kyoto University
Japanese, English, japanese sign language, Korean, American Sign Language
Github Skills (20)
monitoring10
google-cloud-platform10
dashboard10
locust10
python10
machine-learning10
cirq10
gcp10
keras10
ai-platform10
mlops10
tensorflow10
quantum-computing10
jupyter-notebook10
continuous-deployment9
Programming languages (9)
Github contributions (5)
GoogleCloudPlatform/training-data-analyst
Mar 2017 - Mar 2022
Labs and demos for courses for GCP Training (http://cloud.google.com/training).
Role in this project:
ML Engineer
ML EngineerContributions:36 commits, 25 PRs, 5 comments in 5 years
Contributions summary:Etsuji's commits primarily focus on modifications within a machine learning course repository, specifically targeting the development and demonstration of machine learning models. The commits include removing hard-coded directory names and integrating a baby weight prediction application. Furthermore, the user demonstrates an understanding of the TensorFlow ecosystem through the implementation of a Keras model and its deployment on the AI Platform, suggesting a focus on model training and serving. These modifications suggest a role dedicated to building, deploying, and testing machine learning models within the context of GCP training.
gcp
qosf/qml-mooc
Apr 2019 - Jun 2019
Lecture notebooks and coding assignments for the quantum machine learning MOOC created by Peter Wittek on EdX in the Spring 2019
Role in this project:ML Engineer
ML EngineerContributions:11 commits in 2 months
Contributions summary:Etsuji primarily contributed to a Jupyter Notebook related to variational circuits within the context of a quantum machine learning MOOC. Their work included implementing and demonstrating the Quantum Approximate Optimization Algorithm (QAOA), which involves defining operators, setting parameters, and analyzing circuit behavior. The user also worked on a Cirq version of a variational circuits notebook.
machine-learning
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