Applied AI Engineer, ML Infrastructure Engineer / Devops - Emea

Mistral AI Mistral AI · AI Frontier · Munich, Germany · Solutions

Applied AI Engineer focused on DevOps to facilitate customer adoption of Mistral AI products, including deployment, integration, and ensuring optimal production setup from GPU stack to interfaces. Collaborates with researchers and engineers on complex customer projects involving deployment, scaling, and open-source contributions for inference and fine-tuning. Involved in pre-sales calls to understand client needs and provide technical guidance.

What you'd actually do

  1. You’ll be responsible for onboarding customers on our products, providing guidance on deployment and integration, and ensuring the best production setup from the low-level GPU stack up to infrastructure, back-end and front-end interfaces.
  2. You’ll work on deploying state-of-the-art AI applications from consumer products to industrial use cases, driving with our customers a crucial technological transformation.
  3. You’ll work in collaboration with our researchers, other AI engineers, and product engineers on our most complex customer projects involving deployment, scaling, and contributing to our open-source codebases for tasks such as inference and fine-tuning.
  4. You’ll be involved in pre-sales calls to understand potential clients' needs, challenges, and aspirations. You will provide technical guidance on our products and explain Mistral technologies to various stakeholders.

Skills

Required

  • Python
  • Docker
  • Kubernetes
  • CI/CD pipelines
  • AWS
  • Azure
  • GCP
  • Terraform
  • Ansible
  • DevOps
  • Site Reliability Engineering

Nice to have

  • Customer Engineer
  • Forward Deployed Engineer
  • Sales Engineer
  • Solutions Architect
  • Technical Product Manager
  • PyTorch
  • TensorFlow
  • open-source contributions

What the JD emphasized

  • deploying and managing AI-based products in production environments
  • deployment
  • integration
  • inference
  • fine-tuning

Other signals

  • customer facing
  • ML infrastructure
  • DevOps
  • deployment
  • integration