Keith Cuniah
Frontend Engineer at Argilla (formerly Recognai/Rubrix)
Madrid, Community of Madrid, Spain
Summary
🤩
Rockstar🎓
Top SchoolKeith Cuniah is a Frontend Engineer with four years of experience building robust, user-focused web applications from startups to large enterprise clients. Currently at Argilla, he contributes full-stack changes to an open-source AI data curation tool, notably enabling custom base URLs for proxy deployments and refining frontend data flow and token-classification rendering. He brings deep practical expertise in Angular and Vue ecosystems from multiple Sopra Steria engagements, where he led front-end referent duties, introduced testing foundations (Cypress, Jest) and coached teams on UX and library choices. Trained originally in mechanical engineering and scientific programming, Keith combines a methodical, research-minded approach with hands-on UI engineering—often surfacing subtle efficiency and maintainability improvements that avoid obvious quick fixes.
4 years of coding experience
6 years of employment as a software developer
Erasmus, Ingénierie mécanique, Semestre validé, Erasmus, Ingénierie mécanique, Semestre validé at VIA University CollegeLicence, Ingénierie mécanique, Licence, Ingénierie mécanique at Université Pierre et Marie Curie (Paris VI)
French, English, Spanish, créole et pidgin, basés sur le français
Github Skills (14)
front-end-development10
python10
vue-jsx9
backend9
vue9
back-end-development9
javascript9
nuxt9
api8
apidoc8
nlp7
docker6
machine-learning6
dockers6
Programming languages (1)
Github contributions (5)
argilla-io/argilla
Jan 2023 - Jan 2023
Argilla is a collaboration tool for AI engineers and domain experts to build high-quality datasets
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:265 reviews, 10 commits, 193 PRs in 19 days
Contributions summary:Keith implemented a new feature allowing the Argilla server to be launched with a custom base URL, addressing proxy-related deployment scenarios. This involved modifications to the server's settings, static file handling, and frontend configuration to correctly serve the application under a different context path. Furthermore, the user refactored several frontend components to pass only the necessary data, improving efficiency and maintainability. They also addressed a formatting issue that caused an offset in predictions within the token classification interface.
fairness-mlpythontext-annotationdeveloper-toolsnlp-datasets
keithCuniah/vue3-weather-app
Aug 2022 - Aug 2023
Contributions:26 pushes in 11 months
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