Full Stack Engineer

Accountabilities: Build end-to-end product features: Develop and maintain full-stack functionality across frontend interfaces, backend services, and AI integrations, ensuring a seamless user experience. Design intelligent agent workflows: Create multi-step agentic systems capable of planning, executing tasks, interacting with tools, handling failures, and recovering across multiple sessions. Integrate AI technologies: Connect large language models, memory architectures, retrieval-augmented generation (RAG) systems, and external APIs to deliver reliable, context-aware application experiences. Develop real-time user experiences: Architect interfaces that support streaming responses, partial results, responsive interactions, and strict latency requirements. Ensure system reliability: Improve observability, monitoring, error handling, fallback mechanisms, and recovery strategies to maintain dependable AI-driven functionality. Evaluate and improve AI performance: Collect and analyze user feedback, behavioral signals, and telemetry to support model evaluation, reduce hallucinations, and guide continuous product improvements. Design scalable architectures: Collaborate with machine learning and backend engineering teams to establish robust system designs, API structures, and reusable integration patterns. Drive features from concept to production: Take ownership of technical implementation, make independent engineering decisions, and deliver high-quality solutions in an evolving product environment. Champion user-centric engineering: Apply strong product judgment to ensure AI-powered features remain intuitive, resilient to unpredictable model behavior, and useful in everyday scenarios. Requirements: Full-stack development experience: Strong professional experience building applications with modern technologies and frameworks, particularly Next.js, Python, and Node.js. Practical AI engineering expertise: Hands-on experience developing AI-powered applications, implementing RAG systems, or integrating LLM APIs from providers such as OpenAI, Anthropic, or open-source model ecosystems. System design and architecture: Solid understanding of application architecture, API design, distributed systems concepts, and database technologies, including SQL and NoSQL. Reliable AI application development: Demonstrated ability to design robust user experiences that account for network latency, non-deterministic model outputs, graceful degradation, and unexpected failures. DevOps and cloud infrastructure: Experience with containerization, deployment pipelines, and cloud orchestration technologies such as Docker and Kubernetes. Problem-solving and adaptability: Ability to navigate ambiguity, evaluate technical trade-offs, exercise independent judgment, and solve complex engineering challenges. End-to-end ownership: Proven ability to take features from initial concept through development, deployment, and ongoing optimization. Strong product intuition: A user-focused mindset with particular attention to client-side reliability, usability, responsiveness, and the practical value of AI-driven features. Collaboration and communication: Ability to work effectively with cross-functional engineering and machine learning teams while contributing to technical decisions and scalable product development. Location and remote readiness: Based in the Netherlands, with the ability to collaborate effectively within a fully remote, international team. Benefits: Fully remote work: Flexibility to work remotely as part of a globally distributed team. Competitive compensation: Flexible compensation arrangements designed to attract experienced engineering talent. Equity participation: Significant equity opportunities, allowing you to share in the potential long-term success of the product. High-caliber colleagues: Work alongside experienced engineers from leading technology companies in a technically ambitious environment. Greenfield product development: Help build an AI-powered productivity platform from the ground up, with opportunities to influence architecture, engineering standards, and product direction. High-impact ownership: Take responsibility for meaningful technical initiatives and see the direct impact of your work on product capabilities. Advanced AI challenges: Work with agentic workflows, LLM orchestration, long-term memory, real-time streaming, and production-grade AI systems. Collaborative engineering culture: Join a small, highly skilled team that values technical excellence, autonomy, and practical innovation. Professional growth: Expand your expertise at the intersection of full-stack engineering, AI infrastructure, and intelligent product development.