Cian Clarke is a Solutions Principal and seasoned software engineer with 14 years of experience building web applications, front-end engineering and full-stack systems from startup to enterprise. Based in Dublin, he currently leads pre-sales and solution strategy at NearForm, applying expertise in Generative AI, NLP and cloud-native architecture. Previously he scaled engineering teams as VP Engineering at ServisBOT and solved platform-level challenges at Red Hat and Feedhenry, blending hands-on delivery with customer-facing technical leadership. He is a pragmatic coder and tester who has contributed to high-profile open source projects like Rasa—improving S3 model loading robustness and test coverage—demonstrating attention to reliability in ML-driven systems. An entrepreneur since his teens, Cian founded and ran a web agency, giving him product-minded instincts and experience with client delivery, performance tuning and developer evangelism. He pairs deep technical craft with sales-engaged solutioning, helping organisations turn complex AI and cloud problems into production-ready outcomes.
14 years of coding experience
17 years of employment as a software developer
B.A. Mod Hons Computer Science, B.A. Mod Hons Computer Science at Trinity College Dublin
💬 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
Contributions:13 commits, 1 PR, 7 comments in 12 days
Contributions summary:Cian primarily focused on improving the robustness of model loading from S3 within the Rasa framework. Their contributions involved modifying the `AWSPersistor` class to correctly handle S3 namespaces, ensuring that models could be retrieved from specific folder structures. They also added and refined tests to validate the correct parameters passed during model retrieval and decompression, which demonstrates a commitment to thorough testing. Furthermore, the user addressed linting issues and enhanced test coverage, contributing to code quality and reliability.
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