Head of IT & AI Ops

Responsibilities

• Lead and grow the IT & AI Ops organization Manage two Heads of (IT and AI Ops) and their respective teams. Define team structure, operating rhythms, hiring roadmap, and performance standards as the organization scales toward and beyond 20 people.
• Own IT strategy and operations Set the direction for workplace technology, identity and access management, endpoint management, SaaS administration, support operations, asset lifecycle, vendor management, procurement, and documentation. Ensure a consistent, reliable IT experience across Paris, Aubervilliers, and remote contexts.
• Build AI tooling and infrastructure for internal teams Define and execute the AI Ops strategy serving researchers, engineers, calibration, and data teams. Deliver reproducible experiment pipelines, version and data management, CI/CD for ML workflows, observability, and model lifecycle tooling, calibrated to the realities of a quantum hardware environment.
• Own infrastructure, reliability, and security Lead the evolution of internal infrastructure across cloud, on-premise, lab-adjacent systems, networks, storage, and compute environments. Support data-heavy and experiment-driven workflows, including interfaces with software, HPC clusters, and lab systems. Lead the implementation of security controls, access governance, endpoint security, incident response, and business continuity. Drive ISO 27001, NIS2, and related compliance efforts. Protect Alice & Bob's intellectual property through enforceable, scalable, and non-blocking security practices.
• Act as operational technology partner to the COO Own the IT & AI Ops roadmap, budget, and KPIs. Surface risks, infrastructure gaps, and capability investments to executive leadership. Translate operational complexity into clear decisions.

Requirements

10+ years of experience in a relevant technological field, with demonstrated experience managing managers and structuring multi-team organizations.

Strong IT operations foundation: identity and access management, endpoint lifecycle, SaaS stack administration, support operations, vendor management, and security controls at scale.

Solid Cloud & Infrastructure Operations background: multi-cloud architecture and cost governance, infrastructure-as-code, container orchestration, network and storage management, reliability engineering, and capacity planning across production and research environments.

Hands-on Cybersecurity expertise: threat detection and incident response, vulnerability management, security architecture review, compliance frameworks, data protection controls, and embedding security practices across engineering and operational workflows.

Proven MLOps or ML infrastructure experience: reproducible pipelines, experiment tracking, data and model versioning, CI/CD for ML workflows, and observability in research and engineering environments.

Ability to operate across a technically diverse organization: PhD-level researchers, hardware engineers, software engineers, and business stakeholders. You are comfortable with ambiguity, but you do not let ambiguity become chaos: you create structure, make trade-offs explicit, and drive decisions.

Strong execution discipline: roadmap ownership, budget management, prioritization under constraint, measurable delivery. You can zoom out to define strategy and zoom in to debug a process, challenge an architecture, or unblock a team.

Nice to have

Experience in deeptech, quantum, semiconductor, aerospace, HPC, or another R&D-intensive hardware environment.

Experience supporting scientific computing environments: GPU/CPU clusters, SLURM, Kubernetes, distributed systems.

Experience managing IT operations across multiple sites.

Experience with AI tooling governance and secure adoption of LLM tools within a technical organization.