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Mistral AI

Open-weight LLMs and enterprise AI deployment

Enterprise Success Manager

Account ManagementPosted on Nov 13
Full-timeAmsterdam, NetherlandsNo salary listedIn-person

About the Job

Requirements

  • At least 5 years of experience in customer-facing roles such as Customer Success, Delivery/Implementation, or Solutions Consulting, ideally in a technical AI or software-as-a-service environment.
  • Experience owning adoption and commercial outcomes, including retention, renewal, and expansion, for enterprise customers of technical products or deployed solutions.
  • Strong technical fluency in enterprise deployment models, including understanding the implications of on-premises, virtual private cloud, and serverless deployments for available features, telemetry, upgrades, and security reviews, and validating specifics with technical teams.
  • Ability to engage credibly with architects, engineers, information technology, and security teams, translate technical trade-offs into clear business implications for executives and users, and recognize when to involve a deeper technical expert.
  • Experience creating structure or improving processes in an ambiguous, fast-moving environment and motivation to build a company without waiting for detailed instructions.
  • Strong communication, stakeholder management, and execution skills, including aligning teams without formal authority, surfacing risks early, and driving decisions through to completion.
  • Analytical and commercial capability to define meaningful success metrics, assess customer health, and carry renewal and net revenue retention accountability.
  • Fluency in English.

Responsibilities

  • Build an account view of customer objectives, stakeholders, deployed use cases, commitments, and risks, and translate it into a shared success plan with owners, milestones, and measurable outcomes.
  • Establish an operating cadence with customer sponsors, business owners, information technology, and delivery teams, and lead success reviews that clarify progress, risks, decisions, and next steps.
  • Define baselines and track adoption and usage against entitled seats, customer satisfaction, operational health, and business impact; turn evidence into a value narrative.
  • Understand how on-premises, virtual private cloud, or serverless deployment models affect the products and use cases customers can access, operate, and scale, validating specifics with technical teams.
  • Identify the implications of deployment choices for security and data requirements, access and integrations, release and upgrade cadence, available features, capacity, observability, and support processes, and explain trade-offs to customer and company stakeholders.
  • Turn deployment constraints into actionable adoption plans, aligning enablement and release communications with the customer's running version, accounting for security approvals and upgrade windows, and agreeing how to measure usage when telemetry is limited.
  • Identify risks to adoption, renewal, or value, and coordinate the appropriate Solutions, Product, Engineering, or Support owners to address them while setting expectations without unvalidated technical or roadmap commitments.
  • Help customer teams move delivered use cases into regular, effective use by identifying users, workflows, and change-management needs.
  • Coordinate onboarding, training, product updates, and hands-on enablement; develop customer champions and train-the-trainer approaches so adoption can scale beyond direct company involvement.
  • Prepare delivery-to-run transitions by clarifying operational ownership, support and escalation paths, and resources users need to succeed independently.
  • Monitor adoption and customer health, investigate shortfalls in usage or value, and lead coordinated plans to address their causes.
  • Own account renewals by tracking renewal dates and terms, understanding renewal risks, and starting renewal planning early.
  • Track and improve account net revenue retention; identify use cases, teams, and expansion opportunities and share them with the account team based on demonstrated value.
  • Lead business reviews with the account team covering delivered value, owned risks, needed decisions, and roadmap requests.
  • Gather and qualify feedback from users, technical teams, and sponsors; distinguish enablement needs from product gaps, deployment constraints, and support issues, and route feedback to the appropriate owner.
  • Track important customer requests and communicate progress and limitations while Product and Engineering retain ownership of product and technical decisions.
  • Create and improve reusable playbooks, success plans, handover practices, and measurement approaches by testing them with customers.
  • Identify what needs to happen, make plans, mobilize the appropriate people, and follow through in an ambiguous environment.
  • Use roughly one-fifth of working time as protected, rotating build time for creating and improving company processes and practices.

Desired Qualifications

  • Professional French proficiency.
  • Experience with artificial intelligence, machine learning, data platforms, or complex enterprise software, including production deployments in regulated or security-sensitive environments.
  • Experience working alongside deployment, engineering, or professional services teams on custom use cases and transitions from implementation to ongoing operation.
  • Experience building adoption, change-management, or customer education programs across large organizations.
  • Experience at a forward-deployed, solutions-led company.

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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