AI Ops Lead

About Quandela:

Quandela stands as a global leader in quantum computing, driven by groundbreaking technology and a strategic vision for scaling quantum solutions. The company's unique ability to offer both hardware and software solutions, along with its commitment to build energy efficient datacenters and scalability, positions it to play a key role in the next wave of innovation, and in many strategic and sovereign industrial sectors.

Join us at the Forefront of Quantum Computing Innovation 🚀

About this position

You’ll be in charge of building and leading Quandela’s AI Ops Department, under the supervision of the COO. You’ll be designing a robust, resilient and safe AI architecture at the company level, but you will also work a lot alongside the different departments (legal, finance, business, hardware and software engineering) in order to understand their workflows and to implement solutions.

You will share the ownership of this architecture with the IT Manager, who will be responsible for the foundations of the platform (access, devices, data safety…).

You will also be responsible for animating the Quandela AI community, a network of early-adopters championing AI within their teams, by organizing workshops and learning sessions for Quandelians.

Your key responsibilities

· Build and run the internal AI platform: select and integrate the stack (model providers and gateway, approved assistants, retrieval over internal knowledge, evaluation and observability), own the catalogue of approved AI tools, their configuration and their cost, with the IT Manager

· Deliver automations and agents with the teams: collect needs, qualify use cases by impact, feasibility and risk, build workflows and agents

· Drive adoption and skills: animate the network of AI champions, design and run the training program (AI literacy for everyone, role-based sessions, office hours), set up self-service tools so that non-technical teams build and own their own agents

· Own the safety of the system: clear definition of a data access hierarchy, solutions for sensitive data, guardrails for coding agents...

What we are looking for:

• A completed Master’s degree or higher (French Master 2 / Bac+5, an engineering degree conferring the master’s grade, or an equivalent international qualification) in Artificial Intelligence, Machine Learning, Computer Science, Data Science or Applied Mathematics, with strong foundations in software engineering and applied AI. • Examples of relevant European programmes include the M2 Mathématiques, Vision, Apprentissage (MVA) led by ENS Paris-Saclay; the Master’s programmes in Data Science at ETH Zurich and EPFL; the MSc in Machine Learning at UCL; and the MSc in Computing (Artificial Intelligence and Machine Learning) at Imperial College London. These examples are indicative and non-exclusive; equivalent master’s-level programmes are welcome. • 5+ years of experience in software engineering, automation, data engineering or technical product roles. Evidence of personally delivering and maintaining solutions used by teams is essential. • A track record of delivering LLM-based automations , including API integrations and workflow platforms such as n8n or Make. Experience taking solutions beyond the proof-of-concept stage and addressing real-world quality, reliability and cost issues. • Ability to write and maintain integration code in Python or JavaScript/TypeScript, and work with APIs and webhooks. • Working knowledge of retrieval-augmented generation (RAG), evaluation, observability and security for LLM systems. An understanding of context management, knowledge-source quality and permission-aware retrieval. • A product mindset: you keep solutions simple and practical while delivering operational improvements. You can prioritise competing requests and translate business or R&D needs into achievable technical solutions. • Proven ability to lead a community of champions (i.e. early adopters) and manage change across a technically diverse organisation, from PhD researchers to business teams. • Strong data discipline , with the ability to classify data before automating processes, document key decisions and workflows, and measure outcomes. • Professional proficiency in French and English. • On-site presence at least three days per week, with occasional travel between sites (Massy, Palaiseau, Paris, Munich, and our other international subsidiaries).