About this role
⚙️ About Azumuta Manufacturing is entering its biggest transformation in decades. For years, we’ve helped manufacturers digitize their operations through a platform used by leading manufacturers across Europe and North America. From digital work instructions and quality management to traceability, skills and operational excellence, we’ve helped thousands of operators perform their work better, faster and safer. But we’re only getting started. The next generation of manufacturing won’t be driven by software alone. It will be powered by AI that fundamentally changes how work gets done on the factory floor. We’re building a future where work instructions are generated automatically from engineering data, AI agents assist operators in real time, quality documentation is created autonomously, and repetitive administrative work simply disappears. We’re not digitizing existing processes anymore. We’re redesigning manufacturing execution from the ground up. Founded in 2016, Azumuta has grown into one of Europe’s leading Manufacturing SaaS scale-ups, working with some of the world’s most innovative manufacturers across industries including aerospace & defense, industrial machinery, automotive, electronics and medical devices. We’re building what manufacturing will look like over the next decade. If that excites you, you’ll fit right in. 🚀 Ready to join our R&D team? As a Principal Engineer at Azumuta, you help us define what great software engineering looks like in this new reality . You are both a builder and a guide . You cannot do one without the other. You spend a significant part of your time building real product capabilities across our stack ( Node.js , TypeScript, React, PostgreSQL, cloud infrastructure, CI/CD pipelines). At the same time, you help the team rethink how software is built, tested, shipped, and operated so we can move faster without losing ownership, control or understanding of our product and technology. You continuously ask questions like: How do we build our product faster while still owning our code, architecture, and technical decisions ? How do we use AI and agentic development to increase leverage without ending up with a codebase nobody truly understands? How do we scale R&D productivity , not just R&D headcount? How do we run fast experiments and short learning cycles while still shipping reliable SaaS software? You work closely with engineers, product managers, and designers to understand real customer problems, explore where new technologies (including AI) can genuinely add customer value and turn the right ideas into production-ready solutions through fast experimentation and short feedback loops. 🚀 What You’ll Be Doing: Build: what you’ll do hands-on Solve complex product problems Design and implement robust full-stack solutions for complex industrial workflows. Collaborate with Product to translate real-world manufacturing problems into clear, usable software. Own the stack end to end Work across backend APIs, frontend components, data flows, and integrations. Improve performance, reliability, and maintainability across the platform. Ship fast, without breaking things Write clean, scalable, well-tested code. Balance speed with long-term quality and technical ownership. Build secure, scalable SaaS systems Contribute to a platform that is secure, resilient, and ready to scale to thousands of users across multiple markets. Measure and improve Use metrics, incidents, and user feedback to continuously improve reliability, performance, and usability. Guide: how you raise the bar for the team Push modern engineering practices Actively drive disciplined development practices: short feedback loops, strong testing culture, CI/CD automation, and rigorous code reviews. You don’t just follow best practices, you help make them the norm. Shape agentic development responsibly Be a champion for AI-assisted and agentic development. Experiment, set guardrails, and help the team move faster without losing control over product knowledge or system complexity. Improve delivery predictability Challenge slicing, scope, and planning decisions. Help teams move from “busy” to “predictable” without introducing heavy process. Strengthen SaaS operational maturity Bring real-world SaaS experience into everyday decisions: monitoring, alerting, incident awareness, infrastructure as code, and cost-quality trade-offs. Grow engineering leadership Coach engineers through collaboration and example. Over time, help at least one engineer grow into a stronger delivery-focused leadership role. AI agents on the shop floor What happens when software acts autonomously in planning, execution, and verification? Humanoids and advanced robotics What changes when the “operator” is no longer human? Higher autonomy and variability How do we increase flexibility without losing control or traceability? Faster feedback loops How do we shorten the gap between execution, insight, and improvement?