Contract · Senior

Senior PLM Solution Architect

A high-growth cloud infrastructure provider specializing in GPU computing and AI workloads is seeking a technical lead to manage enterprise client relationships. The successful candidate will oversee the technical dimension of client engagements from pre-sales through onboarding and production. This autonomous role involves solving complex infrastructure problems and ensuring large-scale AI workloads run efficiently. You will act as the primary technical bridge between enterprise clients and internal engineering teams to drive successful deployments.

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Opportunity overview

Opportunity type
Contract
Required seniority
Senior
Work location
Remote
Location
France
Start
ASAP
Duration
Freelance
Travel
—

Required role

Solution ArchitectSystem EngineerLead Consultant

Required platforms

KubernetesLinuxNVIDIA GPUCloud Infrastructure

Required skills

Solution DesignInfrastructure as CodeGPU ComputingTechnical SupportPre-salesNetwork ConfigurationStorage ManagementAI Workload Optimization

Required industry experience

Cloud ComputingArtificial IntelligenceHigh Performance Computing

Responsibilities

  • Lead technical onboarding for enterprise clients including network, storage, and server handovers.
  • Act as the primary technical point of contact for diagnosing and resolving complex infrastructure issues.
  • Support pre-sales activities through technical discovery, solution design, and sizing.
  • Conduct Proof of Concepts (POC) with clear technical success criteria to transition clients to production.
  • Debug client workloads directly, including multi-GPU and inference tasks.
  • Proactively monitor account health and identify technical risks or potential blockers.
  • Coordinate with SRE and Engineering teams to translate client needs into technical solutions.
  • Improve onboarding processes by identifying and documenting recurring technical challenges.

Requirements

  • Over 5 years of experience in technical client-facing roles such as Solutions Engineering or Architecture.
  • Strong practical knowledge of Linux, Virtual Machines, and Kubernetes.
  • Deep understanding of Networking, Storage, and NVIDIA GPU infrastructure.
  • Proven ability to diagnose complex AI workload and infrastructure problems.
  • Experience conducting technical demonstrations and enterprise-level deployments.
  • Ability to distinguish between client-side issues and internal engineering requirements.
  • Fluency in English is mandatory; proficiency in French is highly preferred.
  • Proactive mindset with a strong sense of ownership in a dynamic environment.