AI Engineer

Join QuTwo - Europe's private Quantum AI Lab, on our quest to bridge the gap between the classical and the quantum world. We partner with the most advanced and forward-thinking customers, solving fundamental real-world problems, side by side. You will join our team in a full-time capacity, collaborating closely with our scientists, engineers, and customers. In this role, you will apply a blend of exploratory research and practical implementation to design production-grade LLM architectures and autonomous Agentic AI systems that solve complex, real-world problems and drive customer value. We are seeking exceptional AI Engineers. If you are a humble learner, not afraid of the unknown, we encourage you to apply. Role Description As an AI Engineer, you will join our team in a full-time capacity, collaborating closely with our scientists, engineers, and customers. In this role, you will apply a blend of exploratory research and practical implementation to design production-grade LLM architectures and autonomous Agentic AI systems that solve complex, real-world problems and drive customer value. Requirements Proven track record of designing, deploying, and maintaining end-to-end ML systems and production-grade LLM applications at scale. You have owned the transition from prototype to production-hardened service. Minimum of 5 years of professional experience in ML and AI Engineering. You prioritize robust system design, observability, safety/guardrails, and "infrastructure as code" over one-off experiments. Hands-on experience architecting Agentic AI systems, including multi-agent coordination, stateful workflow orchestration, autonomous decision loops, and complex action execution. Strong background in LLM integration, including function calling/tool-use, structured output parsing, and advanced reasoning strategies. Exceptional ability to lead cross-functional initiatives, translate business objectives into technical roadmaps, and uphold high standards for code quality and system reliability. Experience leveling up high-performing engineering teams, conducting deep-dive code/architecture reviews, and fostering a culture of technical excellence. Experience in one or more of the following areas is a plus but not a requirement: Deep experience in LLM infrastructure, including advanced RAG systems, model fine-tuning, agent memory architectures, and automated evaluation frameworks. Proficiency with vector databases, distributed computing, and GPU optimization. Familiarity with combinatorial optimization algorithms or the intersection of ML and Quantum Computing.