HiveMQ is the Industrial AI Platform helping enterprises move from connected devices to intelligent operations. Built on the MQTT standard and a distributed edge-to-cloud architecture, HiveMQ connects and governs industrial data in real time, enabling global leaders like Audi, BMW, Eli Lilly, and Siemens to operationalize AI and drive innovation at scale. At HiveMQ, our culture is Effortless → Empowered → Relentless. We make the complex simple, act with confidence and ownership, and never stop pushing the boundaries of what’s possible. Join us to power the future of intelligent industry. HiveMQ’s Vision for this role We aren't just using AI to help us code; we are an AI-native engineering organization. Our next major milestone is architecting a swarm of autonomous coding bees - specialized agents designed to handle end-to-end feature delivery, from infra provisioning to self-correction. We need an engineer who can define the hive's architecture, the pollination patterns (data flow), and the rigorous guardrails that allow these agents to build - and run - at scale. You will… * Design and evolve the architecture of our autonomous agent swarm — defining agent boundaries, data flows, and the feedback loops that allow bees to self-correct and deliver features end-to-end. * Build and own the infrastructure that powers our AI-native pipeline, including several AWS services like Lambda functions, DynamoDB, SNS/SQS. * Lead with rigorous technical specs — if the logic isn't in the spec, it doesn't exist — translating complex product requirements into precise, agentic-ready designs that serve as the single source of truth for AI-powered implementation. * Steer and evaluate coding agents and LLM models, reviewing their output against a high-quality mental model to ensure correctness, performance, and security. * Extend and maintain our TypeScript/Fastify backend and React micro-frontend architecture, improving observability, reliability, and test coverage across the stack. * Define and enforce guardrails, monitoring systems, and self-healing mechanisms that keep autonomous agents operating safely in production. * Collaborate with Engineering and Product leadership to shape the long-term vision of AI-native development at HiveMQ. * Stay at the forefront of AI tooling, agent frameworks, and LLM capabilities, continuously refining how the hive thinks and builds. You have… * A strong software engineering background (5+ years) with hands-on experience architecting and delivering fullstack applications in TypeScript, Node.js, and React. * Production-level experience with AWS infrastructure — Lambda, DynamoDB, and related services — including an intuitive understanding of where things break at scale. * Worked extensively with AI coding agents and LLM-powered workflows, and know how to steer them effectively rather than just prompt them. * A DevOps-first mindset — you've built the monitoring, alerting, and self-healing systems that keep production environments healthy without constant human intervention. * Solid architectural skills around bounded contexts, idempotent design, decoupled micro-frontends, and distributed systems. * The ability to translate ambiguous product goals into precise, agentic-ready technical specs with no room for misinterpretation. * A high sense of ownership and agency — you proactively identify bottlenecks, propose solutions, and follow through without being prompted. * Excellent English communication skills and the ability to collaborate effectively across engineering, product, and leadership in a remote-first environment. EXCERPT FROM OUR CUSTOMER LIST https://www.hivemq.com/customers/ Informations about our job advertisements Job advertisements of HiveMQ GmbH are always directed at female, male and various applicants, regardless of age, gender, religion, sexual identity, disability, race, ethnic origin, world view, etc. The selection of a candidate is exclusively based on qualifications. For organisational reasons, we cannot return application documents and cannot reimburse any expenses that you incur during the application process.