AI Systems Engineer

Rapyuta Robotics, an ETH Zurich spin-off, operates with the goal of making robots more accessible to anyone in the logistics industry. Rapyuta Robotics seeks individuals who believe collaboration drives better and more sustainable outcomes, and who are fearless in challenging boundaries to create meaningful impact through technology. 

As an AI Systems Engineer, you will design and scale enterprise AI platforms and backend systems powering intelligent automation and large-scale workflows. You will work across AI/ML, cloud infrastructure, and backend engineering to build scalable solutions including RAG pipelines, AI agents, MCP services, and cloud-native microservices, while collaborating with global stakeholders to turn business needs into reliable technical solutions. 

Responsibilities

• AI Systems Development & Innovation  Design and develop scalable AI platforms and backend systems including RAG pipelines, AI agents, MCP services, and cloud-native microservices; drive innovation by exploring and adopting emerging AI/ML technologies, and act as an advocate for AI assisted engineering & operations by driving adoption and popularization of advanced AI capabilities across teams and products. 
• System Delivery & Collaboration  Seek the needs and opportunities to deliver clear technical solutions, collaborate with cross-functional and global stakeholders, ensure reliable delivery of scalable AI applications, and continuously improve performance, maintainability, and operational efficiency across systems and workflows. 
• Technical Architecture & Engineering Excellence  Contribute to system architecture, infrastructure automation, and engineering best practices across cloud platforms, operations, CI/CD, observability, testing, and rapyuta.io deployments; promote high standards in scalability, reliability, and long-term platform sustainability. 

Requirements

• Experience designing and delivering complex, scalable AI and backend platforms with quality, reliability, and long-term maintainability in production environments.   
• Strong ownership mindset with the ability to shape product and technical direction through collaboration, clear communication, empathy, and sound engineering judgment.  
• Expected to translate stakeholder needs into effective solutions while mentoring others, sharing feedback, and elevating team capability.  
• Specialization in and passion for AI Systems Engineering, including one or more areas; 
  • AI/ML Systems & Agents: Production-grade LLM applications, RAG pipelines, AI agents, or multi-agent orchestration systems beyond experimental use cases.  
  • Backend & Distributed Systems: Delivered scalable microservices and event-driven architectures supporting high-volume, low-latency workloads in cloud environments.  
  • ML Ops & AI Infrastructure: Designed and operated ML pipelines, model deployment systems, and infrastructure automation using modern DevOps and cloud-native tools, ensuring reliability, observability, and cost efficiency. 

More Details 

Making Robotics More Accessible  Use AI and LLM technologies to simplify interaction with complex robotics systems and make advanced automation more usable in real-world operations. Build human-centered interfaces and interaction layers that translate intent into action, surface insights clearly, and enable natural language-based control and observability of AI and robotic platforms. All solutions are designed with human operators in mind, ensuring clarity, usability, and establishing trust between our customers and their systems. 

AI and robotics in the logistics domain  Apply AI systems engineering expertise to logistics and warehouse robotics environments by building scalable AI-driven backend systems that support automation, coordination, and operational intelligence across complex distributed systems. This includes developing AI-enabled services, orchestration layers, and data-driven platforms that improve reliability, observability, and decision-making in large-scale operations. Experience in logistics, robotics, or other complex distributed system domains is highly beneficial.