Applied AI Engineer, Prototyping

Mistral AI Mistral AI · AI Frontier · Paris, France · Solutions

Applied AI Engineer role focused on building and delivering full-stack AI solutions for enterprise customers within short timelines (4-8 weeks). The role involves customer engagement, technical leadership, end-to-end system ownership, and contributing to product innovation by testing new capabilities and translating real-world usage into product direction. Requires strong software engineering fundamentals, experience with LLMs, RAG, and agentic systems, and a builder mindset.

What you'd actually do

  1. Build and deliver full-stack AI solutions for global customers, owning the end-to-end execution from scoping to deployment as technical lead.
  2. Engage directly with customers to understand use cases, define requirements, and translate them into robust technical architectures and working systems.
  3. Collaborate across GTM, product, and engineering to ship solutions and contribute to internal tools, product improvements, and open-source initiatives.
  4. Solve complex applied AI problems across industries, working on real-world GenAI use cases and providing hands-on technical guidance throughout engagements.

Skills

Required

  • Fluent in English
  • Strong problem-solving mindset
  • Building robust and scalable solutions
  • Balancing speed with precision
  • Methodical approach to technical execution
  • Strong communication skills
  • Client-focused
  • Comfortable collaborating across diverse teams
  • 2+ years experience as a hands-on engineer shipping AI-powered products
  • Strong track record of building and deploying production systems end to end
  • Comfortable working across the modern AI stack: LLMs, RAG, agentic systems
  • Strong software engineering fundamentals in Python
  • Experience building scalable backend systems (e.g., FastAPI, Pydantic)
  • Working understanding of frontend development (e.g., React or Vue)
  • Broad technical range: system design, infrastructure (e.g., Kubernetes), applied LLM problem-solving
  • Strong bias toward shipping

Nice to have

  • Experience with Docker, Kubernetes, cloud platforms (AWS or GCP), and infrastructure tooling such as Terraform
  • Contributed to open-source projects, ideally in the LLM or applied AI space
  • Strong full-stack + AI experience, having built and shipped end-to-end applications (e.g., FastAPI, Next.js)

What the JD emphasized

  • production-grade AI systems
  • production systems
  • real-world applications
  • real-world usage
  • real-world challenges

Other signals

  • building production-grade AI systems
  • translating ambiguous business problems into working software
  • shipping full-stack systems end to end
  • testing new capabilities, pushing systems to their limits
  • turning real-world usage into product direction