Staff / Senior Staff Software Engineer – AI Systems

CKSource builds CKEditor , an open-source rich text editor used by millions worldwide. We're part of Tiugo Technologies, alongside TinyMCE, ButterCMS, and Uploadcare. Your role: We're looking for a hands-on Staff / Senior Staff Software Engineer to drive the architecture and technical direction of backend of AI-powered systems in CKEditor. CKEditor AI is already in production, with features like AI Chat, Quick Actions, and Review. We're expanding our AI platform, integrating LLMs, contextual data, APIs, and external systems. This is an individual contributor role, not an ML research or people-management position . You'll combine strong software engineering fundamentals with the challenges of building reliable, scalable AI-powered products.
Requirements:
To be successful in this role you need the following: Proven experience designing and delivering complex production systems at Staff, Senior Staff, Tech Lead, or equivalent level. Strong hands-on experience with TypeScript, Node.js , software architecture, and distributed systems . Experience building LLM- or AI-powered features in production , including testing, evaluation, observability, and reliability. Strong architectural judgment and the ability to balance simplicity, scalability, and long-term maintainability. End-to-end ownership, from ambiguous technical challenges to successful production delivery. Ability to influence technical decisions, mentor engineers, and collaborate effectively across teams without formal authority.
Must have: Node.js, TypeScript, AI, LLM
Nice to have: Multi-provider LLM integrations, AWS, SDK, open-source products
Daily tasks: - Lead the architecture and technical direction of AI-powered systems, from early exploration to production and beyond.
– Design scalable, maintainable systems, making sound decisions about APIs, service boundaries, reliability, performance, security, and cost.
– Turn complex product ideas into experiments, prototypes, and production-ready solutions.
– Build reliable LLM integrations and establish practices for testing, evaluation, monitoring, and improving AI features.
– Stay hands-on by writing and reviewing code, solving complex technical challenges, and mentoring engineers.
– Influence engineering practices and help teams adopt AI development tools without compromising code quality or maintainability.