Applied Ai, Forward Deployed Machine Learning Engineer - Munich

Mistral AI Mistral AI · AI Frontier · Munich, Germany · Business

Applied AI Engineer role focused on deploying Mistral's AI solutions for enterprise clients, involving fine-tuning, LLM applications, inference, and pre-sales technical guidance. Bridges the gap between research and real-world applications.

What you'd actually do

  1. You’ll individually help deploy into production use cases with a considerable business impact across various industries.
  2. You’ll work on state-of-the-art GenAI 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, product engineers on our most complex customer projects involving complex fine-tuning, state-of-the-art LLM applications, and contributing to our open-source codebases 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.
  5. Your collaboration with our product and science team to improve continuously our product and model capabilities based on customers’ feedback

Skills

Required

  • English fluency
  • 2+ years as a technical individual contributor (data scientist or software engineer) on AI-based products
  • Proven experience in AI or machine learning product implementation with APIs, back-end and front-end interfaces
  • Experience in Fine Tuning LLMs, tackling advanced RAG or agentic use cases
  • Deep understanding of concepts and algorithms underlying machine learning and LLMs
  • Strong technical coding skills in Python
  • Strong communication skills with an ability to explain complex technical concepts in simple terms with technical and non-technical audiences

Nice to have

  • Contributed to open-source projects in particular in the space of LLMs
  • Experience as a Customer Engineer, Forward Deployed Engineer, Sales Engineer, Solutions Architect or Technical Product Manager
  • Experience with deep learning with Pytorch

What the JD emphasized

  • customer-facing technical organization
  • startup CTOs
  • own end-to-end project execution
  • bridge the gap between cutting-edge AI research and real-world enterprise applications
  • AI or machine learning product implementation
  • Fine Tuning LLMs
  • advanced RAG
  • agentic use cases
  • deep understanding of concepts and algorithms underlying machine learning and LLMs
  • strong technical coding skills in Python
  • explain complex technical concepts in simple terms

Other signals

  • customer-facing technical role
  • deploy cutting-edge AI solutions
  • bridge the gap between research and applications
  • fine-tuning
  • LLM applications
  • open-source codebases
  • inference
  • pre-sales calls
  • customer feedback