Applied AI Engineer, AI for Mistral

Overview

Mistral.ai is hiring an Applied AI Engineer for the AI for Mistral team, based in Paris, France. The mission of this role is to make Mistral AI-native internally by leading the design and delivery of production-grade AI systems across multiple departments such as go-to-market, human resources, engineering, and finance. The position operates at the intersection of product, engineering, and business to convert advanced technology into scalable, real-world solutions.

Who can apply

Candidates should have a minimum of three years of experience building and deploying production artificial intelligence software. Applicants need strong full-stack engineering skills in Python, TypeScript, scalable backends like FastAPI or Node, and enough frontend experience with React or Vue to independently ship a usable application. Familiarity with modern generative AI use cases, including retrieval-augmented generation, agents, workflows, orchestration, and evaluation, is required. Candidates must possess fluent English communication skills, a bias for delivery, strong ownership, and familiarity with AI-native tools.

What you get

The position is a full-time employment opportunity located in Paris, France. A comprehensive benefits package is provided to support well-being, growth, and work-life balance, which varies by country and may include healthcare coverage, parental leave, retirement plans, relocation support, wellness programs, and meal and transportation allowances. Visa sponsorship may also be offered for suitable candidates.

Requirements

Applicants are expected to have hands-on experience with vibe-coding tools alongside a commitment to clean code and architecture. The role requires conducting discovery with department heads, mapping workflows, translating ambiguous requirements into prioritized use-case backlogs, and delivering end-to-end full-stack applications and AI systems. Additional preferred backgrounds include exposure to legal, public affairs, marketing, or compute infrastructure domains, data engineering or analytics experience, open-source contributions regarding large language models, and a builder mindset for internal tooling.