Fábio Uechi
Machine Learning Engineer (MLOps) at Halter
Auckland, Auckland, New Zealand
Summary
👤
Senior🎓
Top SchoolFábio Uechi is a Machine Learning Engineer specializing in MLOps with 15 years of experience building scalable, cloud-native systems from architecture to production. Currently at Halter after a three-year MLOps stint at Imagr, he blends site reliability, full-stack and ML engineering skills to deliver reliable deployment pipelines and production-grade ML services. His background includes leading multi-team engineering efforts, designing high-throughput systems on Google Cloud and AWS, and prototyping recommender systems and big-data pipelines. An active open-source contributor, he has improved both back-end and UI components in well-known projects like Locust and extended Togglz with Datastore and Memcache support for robust feature flagging. Comfortable across Java, Python and cloud platforms, he brings a pragmatic craftsmanship mindset to complex operational and data challenges.
15 years of coding experience
19 years of employment as a software developer
Bachelor of Science (BSc), Computer Science, Bachelor of Science (BSc), Computer Science at Universidade Federal de São CarlosAssociate's degree, Industrial Electronics Technology/Technician, Associate's degree, Industrial Electronics Technology/Technician at Liceu de Artes e Ofícios de São Paulo
Stackoverflow
827reputation
39kreached
17answers
12questions
Github Skills (28)
locust10
python10
jinja210
loadtest10
feature-flags10
google-app-engine10
google-cloud-datastore10
java10
webui10
javas10
argument-parsing10
memcached10
load-testing10
jtest9
junit9
Programming languages (20)
Github contributions (5)
togglz/togglz
Sep 2013 - Jan 2017
Feature Flags for the Java platform
Role in this project:
Back-end Developer
Back-end DeveloperContributions:17 commits, 3 PRs, 9 comments in 3 years 4 months
Contributions summary:Fábio's commits primarily focus on adding and modifying features related to App Engine and Google Cloud Datastore integration. They implemented a Datastore-based state repository for feature flags, allowing for persistent storage and retrieval of feature states within the application. Furthermore, the user refactored the code to improve its structure and introduced a Memcache-based caching mechanism to optimize performance. This resulted in enhancements to the project's core functionality for feature toggling.
continuous-deploymentfeature-toggleplatform-javagocdflags
locustio/locust
Sep 2021 - Sep 2021
Write scalable load tests in plain Python 🚗💨
Role in this project:Back-end Developer & UI Developer
Back-end Developer & UI DeveloperContributions:16 commits, 2 PRs, 7 comments in 9 days
Contributions summary:Fábio primarily focused on enhancing the web UI of the Locust load testing tool. Their contributions included adding new Jinja2 blocks for improved extensibility, exemplified by injecting custom CSS. They also made changes to the argument parser, introducing features to exclude arguments from the UI and refactoring code. Furthermore, the user updated example code and made general improvements to the codebase.
scalablestress-testingbenchmarkingpythonload-testing
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