Head of AI

About the role

This is a senior, hands‑on leadership position for an AI visionary who will shape intelligent business automation within a fast‑growing SaaS company. Based in Switzerland and operating in a remote‑first environment, you will work closely with executive leadership to define a long‑term AI strategy while delivering production‑ready solutions that directly impact customers’ operational workflows.

Key responsibilities

• Design, architect, and evolve scalable AI systems, including AI agents, LLM integrations, automation workflows, and retrieval‑augmented generation (RAG) knowledge frameworks.
• Build and deploy production‑ready AI solutions that integrate deeply with ERP and other operational business processes.
• Collaborate with executive leadership to define AI strategy, identify automation opportunities, and align technology initiatives with growth objectives.
• Lead by example through hands‑on coding, system design, and implementation of event‑driven architectures and intelligent automation pipelines.
• Recruit, mentor, and develop a high‑performing AI engineering team while fostering an execution‑oriented culture.
• Partner with Product, Engineering, and business stakeholders to drive AI adoption and ensure customer‑focused outcomes.
• Establish scalable infrastructure, reliability standards, and best practices for AI system performance, maintainability, and security.
• Act as the internal AI expert, educating teams on capabilities, limitations, and strategic applications of emerging AI technologies.

Required profile

• Proven experience as a Staff Engineer, Principal Engineer, Technical Founder, or similar senior technical leadership role in AI‑driven environments.
• Strong expertise in backend architecture, distributed systems, API design, and scalable cloud‑based infrastructure.
• Deep hands‑on experience with LLM orchestration, AI agents, retrieval‑augmented generation, automation systems, and data pipelines.
• Demonstrated ability to move rapidly from concept to production‑ready implementation while maintaining quality and security.

Required skills

• Backend architecture
• Distributed systems
• API design
• Cloud‑based infrastructure
• LLM orchestration
• AI agents
• Retrieval‑augmented generation (RAG)
• Automation systems
• Data pipelines
• Event‑driven architectures
• Intelligent automation pipelines