Aciel Eshky
Sr Product Manager Tech, Generative AI
City of Edinburgh, Scotland, United Kingdom
Summary
🤩
Rockstar🎓
Top SchoolAciel Eshky is a Sr Product Manager in Generative AI at Amazon with eight years of experience bridging machine learning research and product delivery. He holds a PhD from the University of Edinburgh and has transitioned academic ML expertise into industry roles, including senior research and product leadership at Rasa where he worked on production-ready conversational AI. His background spans postdoctoral research, statistical modelling, and hands-on engineering—contributing to the popular open-source Rasa framework by improving stability, tests, and release readiness. Known for blending rigorous evaluation with product focus, he specializes in moving models from research prototypes into reliable, testable systems at scale. Based in Edinburgh, he combines deep technical fluency with practical product instincts and a track record of making complex ML projects production-ready.
7 years of coding experience
5 years of employment as a software developer
Doctor of Philosophy (Ph.D.), Doctor of Philosophy (Ph.D.) at The University of EdinburghBachelor of Science (B.Sc.), Bachelor of Science (B.Sc.) at King Saud University
English, Arabic
Stackoverflow
95reputation
5kreached
3answers
3questions
Github Skills (13)
machine-learning10
rasa10
pytest10
python10
natural-language-processing9
chatbot9
bot-framework9
git7
linker6
double6
boost6
visual-studio6
executable6
Programming languages (4)
Github contributions (5)
RasaHQ/rasa
Jul 2021 - Nov 2021
đź’¬ Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants
Role in this project:
Back-end Developer & Test Automation Engineer
Back-end Developer & Test Automation EngineerContributions:158 reviews, 32 commits, 38 PRs in 4 months
Contributions summary:Aciel contributed to the Rasa framework by removing experimental feature warnings and preparing releases, indicating a focus on improving stability and preparing for production. They also made changes to configuration files, specifically focusing on the implementation of markers. Their work also involved modifications to tests and adding new tests within the framework, which contributes to the testing and reliability of the project. The user worked to integrate the evaluation markers into the project, making them a key contributor to ensuring model success.
nlupythonbotspeech-recognitionbotkit
UltraSuite/ultrasuite-tools
Jun 2018 - Nov 2019
Contributions:24 commits, 17 pushes, 1 branch in 1 year 4 months
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