AI Automation Engineer
Your role at C12 Quantum Electronics:
Transversal Solution Architecture: Partner with teammates across R&D, IT Operations, and Software teams to map their workflows and design end-to-end AI systems that solve their specific operational challenges
Self-Hosted LLM Infrastructure: Deploy, maintain and optimize local LLM infrastructure (Ollama, vLLM, or similar) to power intelligent automation while ensuring data security and compliance with research confidentiality requirements
Hybrid Automation & Agentic Systems: Build robust pipelines using workflow orchestration tools, python for custom logic and agentic frameworks powered by self-hosted models for intelligent reasoning
Full-Stack Prototyping: Own the full lifecycle-from identifying an opportunity to shipping a production-ready internal tool (e.g. automated documentation systems, intelligent task management or infrastructure monitoring agents).
Extending AI Capabilities: Develop and maintain MCP (Model Context Protocol) servers and API integrations to give our self-hosted agents secure access to internal systems (Google Workspace, Nextcloud, monitoring tools) and quantum computing platforms.
AI Observability & Iteration: Implement feedback loops to track the performance and reliability of your automations, moving from "vague prompts" to deterministic, high-quality outputs.
Infrastructure Integration: Work closely with existing infrastructure (OVH cloud, Tailscale VPN, Ansible automation) to deploy secure, scalable AI-powered solutions on our private infrastructure
We are seeking our first AI Automation Engineer to act as a technical force multiplier across the entire organization. This is a transversal role: you will sit at the intersection of IT/Software/Infrastructure, R&D and Operations to identify operational bottlenecks and solve them with intelligent systems.
You won't just be "automating tasks", you will be architecting stateful AI workflows and deploying autonomous agents that handle complex, multi-step logic using self-hosted LLM infrastructure. You will balance rapid iteration with production-grade engineering to build a truly AI-native quantum computing organization while maintaining complete data sovereignty.
Key responsibilities:
About you:
Builder Mindset: You are an "AI-native" engineer who excels at turning ideas into working systems with a portfolio of personal projects or previous experience showing you can build "end-to-end.
Orchestration: You have experience with Prefect, LangGraph, n8n, or similar workflow engines that can integrate with self-hosted LLM endpoints.
Languages: You have strong proficiency in Python (for data processing, automation, and agent logic).
LLM Engineering: You have a deep understanding of RAG, tool-calling, Prompt Engineering and MCPs. Experience adapting these techniques for open-source models and local deployments is a plus
Infrastructure & DevOps: You have a solid understanding of cloud infrastructure (OVH preferred), containerization (Docker), GPU management, VPN solutions (Tailscale) and configuration management (Ansible).
Transversal Communication: You can translate a "business pain" into a "technical implementation" and explain your architectural choices to both technical and non-technical peers, including quantum researchers.
Security-First Approach: You have a strong understanding of secure automation practices, data sovereignty and the importance of keeping sensitive research data on-premises. Experience with private networking and access control is a plus
Adaptability: You thrive in ambiguity and are excited by the prospect of touching every part of a growing deep-tech quantum computing startup.