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Mistral AI

Open-weight LLMs and enterprise AI deployment

Design & Reliability Engineering Manager

Engineering LeadershipConfirmed live in the last 24 hours
Full-timeÎle-de-France, FranceNo salary listedHybrid

About the Job

Requirements

  • At least 8 years of experience in mission-critical data center design, engineering, commissioning, or technical reliability, including responsibility for complex technical decisions.
  • A degree in electrical or mechanical engineering, or equivalent professional experience, with deep knowledge in one discipline and strong working knowledge of the other.
  • Demonstrated expertise in critical power and cooling architecture, redundancy, failure-mode analysis, and operational risk management.
  • Experience reviewing engineering drawings, calculations, commissioning evidence, and root-cause analyses, and coordinating technical specialists.
  • Ability to influence colocation providers and cross-functional stakeholders.
  • Excellent English communication and willingness to travel.

Responsibilities

  • Own and continuously improve technical design criteria and colocation requirements for new facilities, expansions, and retrofits, including availability objectives, failure domains, maintainability, and operational flexibility.
  • Translate GPU platform roadmaps into measurable facility envelopes, including IT load and transients, rack density, electrical topology, redundancy, cooling capacity, supply and return temperatures, flow, pressure, water quality, and monitoring.
  • Review utility and medium-voltage/low-voltage arrangements, transformers, generators, UPS, switchgear, busway, protection coordination, earthing, power quality, and metering for suitability under demanding AI loads.
  • Review mechanical and controls architecture, including chilled-water and heat-rejection systems, pumps, control sequences, CDU integration, leak detection, filtration, water treatment, and cooling recovery scenarios.
  • Lead multidisciplinary reviews of single-line diagrams, P&IDs, sequences of operation, design calculations, capacity models, failure-mode studies, and proposed deviations; document risks and decisions.
  • Participate in site selection, technical due diligence, provider RFPs, design milestones, and commercial technical schedules, challenging assumptions before they become delivery constraints.
  • Define design review and exception processes that balance performance, resilience, deployment speed, efficiency, capital cost, and long-term operability.
  • Partner with delivery and commissioning teams to define functional and integrated testing requirements, acceptance criteria, evidence packages, and outstanding-defect management.
  • Validate that electrical and cooling infrastructure can support the contracted AI load envelope under normal, maintenance, and credible fault conditions.
  • Review test scripts and results for loss of utility, UPS/generator transitions, cooling component failures, controls faults, CDU interface events, and staged restoration where applicable.
  • Ensure operational handover includes accurate drawings, asset and interface registers, monitoring points, setpoints, maintenance constraints, emergency procedures, and unresolved risk ownership.
  • Support live-site changes and capacity upgrades with a disciplined review of impact, sequencing, rollback plans, and operational readiness.
  • Act as a technical escalation point for recurring or high-impact electrical, mechanical, and controls incidents across operational colocation sites.
  • Analyze alarms, BMS/EPMS trends, event sequences, power-quality records, cooling telemetry, commissioning findings, and maintenance evidence to distinguish symptoms from systemic causes.
  • Lead or challenge technical root-cause investigations with colocation partners, including common-mode failures, protection miscoordination, thermal instability, control logic, hydraulic performance, and hidden capacity limitations.
  • Develop risk-ranked corrective actions and reliability improvement programs; validate effectiveness through evidence and post-change monitoring.
  • Identify fleet-wide patterns and feed lessons learned into reference designs, acceptance tests, technical specifications, and provider governance.
  • Review maintenance and change proposals that create material exposure to compute availability, providing clear technical recommendations and escalation where required.
  • Work with IT hardware, network, and platform teams to understand the operational consequences of rack-level and cluster-level failure domains.
  • Influence colocation providers, consultants, and OEM partners through technically rigorous, fact-based reviews without relying on direct operational authority.
  • Present design trade-offs, reliability risks, and investment recommendations in language accessible to engineering leadership and business stakeholders.
  • Develop reusable standards, technical playbooks, and engineering knowledge sharing to support consistent decisions across a rapidly expanding portfolio.

Desired Qualifications

  • Experience with AI/HPC infrastructure, high-density accelerator platforms, and direct-to-chip liquid cooling.
  • Knowledge of CDUs, facility-to-technology cooling interfaces, hydraulic performance, cooling-water quality, and leak management.
  • Experience with electrical protection/selectivity, power quality, controls sequences, integrated systems testing, and BMS/EPMS analytics.
  • Experience creating technical standards across multiple sites or providers, including design deviations and fleet-wide reliability improvements.
  • Professional engineering registration or equivalent technical credentials.

About the Company

Mistral AI

Open-weight LLMs and enterprise AI deployment

Mistral AI builds open-weight large language models and the tools to run them, along with inference infrastructure and enterprise products that let organizations deploy AI with control over data residency, customization, and the underlying stack. Its platform provides open-model architectures and a deployment stack for inference, governance, and integration that can run on‑premises or in the cloud. This openness and modular stack differentiate it from single-vendor hyperscalers by prioritizing data locality and flexibility. The company aims to enable reliable, customizable AI deployment at scale for businesses and public-sector customers across many countries.

Company size
1,001-5,000
Company stage
Series D
Total funding
$7.2B
Headquarters
Paris, France
Founded
2023

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