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Join us as an AI Engineer in our AI Products team to make sure the AI agents we ship are as reliable as they are fast to build. You'll design the evals, observability and tooling that keep agent quality high in a regulated bank.
Build agents: You'll develop agents that take real actions for customers.
Match rigor to risk: You'll size evaluation to the stakes, because an agent making transfers is not a PR bot.
Own evaluation of every feature: You'll make sure every new agent capability comes with the right evaluation strategy, defining what needs to be tested offline and validated online, based on business requirements, expected outcomes and risk.
Observe agents in production: You'll build the monitoring strategy that makes agent behaviour visible, tracks performance against business objectives and flags issues early.
Scale it as a platform: You'll turn our tools, evaluation practices and monitoring capabilities into a self-service platform that lets other teams build, evaluate and operate their own agents.
Evaluation mindset: You've evaluated models or LLM systems and know what makes a test misleading.
Strong ML foundations: You understand the ML lifecycle, from training and iteration to evaluation and monitoring, and core concepts such as overfitting, generalization and cross-validation.
LLM and agentic systems knowledge: You understand the fundamentals of LLMs (particularly the Transformer architecture) and the building blocks of agentic systems. You know how to optimize an agent's harness (prompts, tools, hooks and orchestration) to improve its reliability and performance.
Builder instinct and backend comfort: You've built and shipped something used by real users, whether professionally, during an internship or as a side project. You're comfortable reading and writing backend code, working with APIs and integrating systems.
AI-native curiosity: You follow developments in AI closely, experiment with new approaches on your own and use AI tools daily in your work.
A rare problem: You'll make AI agents trustworthy in a regulated bank, where quality is not optional.
Hands-on coaching: You'll learn directly from Pierre-Jean, one of the AI engineers in the team.
Room to grow: Your scope grows as the platform opens to more teams.
Space to experiment: You'll work at the frontier of AI in a team that keeps experimentation at its core.
Your manager will be Sophie Cornay, our Product Leader for AI Products.
Her background? Sophie has led product teams in startups and established tech companies.
What does she bring to the team? She gives engineers real ownership and space to experiment.
Python and Pydantic-ai: Python is our baseline language, and Pydantic-ai is our agentic framework.
AWS: We access our models through AWS.
Grafana: We track usage and cost per sub-agent on it.
GitHub and CI: Every agent change ships through a reviewed pull request.
Braintrust: Evaluation and agent monitoring run on our self-hosted instance.
[DIVERSITY] At Qonto, we understand that true diversity isn’t just about ticking boxes on a hiring checklist. Apply regardless of the boxes you tick - who knows? You may have the missing piece of the puzzle we’ve been searching for all along.
[HIRING PROCESS DURATION] On average, our hiring process lasts 20 working days. More information on our candidate journey here
[DATA RETENTION] By applying, you agree that Qonto processes your personal data to assess your application. Your data is kept for up to 2 years in our candidate pool. Read our Privacy Notice for full details.
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