Full Stack Engineer, AI systems About the RoleA1 is building a proactive AI chat app for everyday users to bring intelligence to conversations, errands, organising and workflows. Unlike traditional chat-based applications, our product focuses on achieving high reliability for long-running workflows, persistent context, and real-world task completion. The system must handle multi-step reasoning, interact with external tools, and remain reliable despite non-deterministic model behavior.We are looking for a Full Stack Engineer - AI Systems to build the product layer that turns these capabilities into usable, production-grade workflows. This includes designing how agents operate, fail, recover, and deliver consistent value to users. FocusBuild end-to-end product features across frontend, backend, and AI integrationsDesign agent workflows that handle planning, tool use, failure, and recovery across multiple steps.Integrate LLMs, memory, and external tools into systems that behave reliably under real-world conditionsDesign real-time AI interactions with streaming, partial results, and tight latency constraintsImprove system reliability, observability, and fallback mechanismsCollaborate closely with ML, backend, and product teams to ship features end-to-endContinuously iterate based on real usage and failure modes Ideal ExperiencesStrong experience in full stack engineering (frontend + backend)Solid understanding of system design and API architectureExperience working with LLMs, RAG systems, or AI-powered applicationsAbility to handle ambiguity and make pragmatic engineering decisionsStrong ownership - able to take features from idea to productionComfort working in fast-moving environments with evolving requirements OutcomesOwn and ship AI-native product features that move beyond chat into persistent, goal-driven workflowsDesign and deploy agent workflows that reliably complete multi-step tasks across tools and sessionsReduce latency and improve responsiveness of AI interactions while maintaining output qualityBuild robust fallback and recovery mechanisms for LLM and tool failures in production environmentsImprove the success rate and reliability of AI-driven workflows through iteration, evaluation, and monitoringEstablish patterns and abstractions for integrating LLMs, memory, and external tools into scalable product systemsContribute to a product experience where AI feels proactive, consistent, and dependable over time Tech StackNext.jsPythonNodeJsPytorchOpenAI / Anthropic / open-source LLMsSQl & noSQLKubernetesDocker How We WorkThe best products today in the world were built by small, world class teams. We are a high talent density and hands-on team. We make decisions collectively, move at rapid speed, striking a balance between shipping high quality work and learning. Joining our team requires the ability to bring structure, exercise judgment, and execute independently. Our goal is to put in hands of our users a truly magical product Interview processIf there appears to be a fit, we'll reach to schedule 3, but no more than 4 interviews.Applications are evaluated by our technical team members. Interviews will be conducted via virtual meetings and/or onsite.We value transparency and efficiency, so expect a prompt decision. If you've demonstrated the exceptional skills and mindset we're looking for, we'll extend an offer to join us. This isn't just a job offer; it's an invitation to be part of a team that's bringing AI to have practical benefits to billions globally.