Senior AI Engineer New

[OUR MISSION AND CUSTOMERS] We are creating the freedom for SMEs to succeed by delivering Europe's leading finance workspace with banking at its core, augmented by financial tools. We are proud to be rated 4.7 on Trustpilot, https://www.trustpilot.com/review/qonto.com based on 60,000 reviews. Our culture https://qonto.com/en/careers/culture puts customer satisfaction at the core of what we do (more about our culture here https://medium.com/qonto-way/the-qonto-way-our-culture-is-our-strategy-90defd4895f9).

[OUR JOURNEY] Founded in 2016 and launched in 2017 by Alexandre https://www.linkedin.com/in/aprot/ and Steve https://www.linkedin.com/in/steveanavi/, Qonto has grown to 1,700 Qontoers serving over 750,000 customers across 8 European countries. We have been profitable since 2023, and we are just getting started.

[OUR BELIEFS] We hire for skills and potential. With 80+ nationalities, 45% women, and 56% of women in our leadership team, diversity isn't a program; It's who we are. We've built a discrimination-free hiring process https://medium.com/qonto-way/a-recruiters-dilemma-confronting-bias-in-hiring-b78acce148eb because the best teams are built on merit.

[AI AT QONTO] AI is deeply embedded in how we work (here https://medium.com/qonto-way/ai-at-qonto-vision-statement-725a7e2f659b) - Every Qontoer gets unlimited access to the best AI tools. We want people who experiment without waiting for permission, push AI beyond the obvious, know when to trust it, and when to question it.

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.

➡️ What you'll do

- 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.

➡️ What we're looking for

- 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.

➡️ What we can offer you

- 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 future manager

Your manager will be Sophie Cornay https://www.linkedin.com/in/sophie-cornay-16101a23/, 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.

➡️ Our stack and practices

- 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 https://qonto.com/en/careers/candidate-journey

[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 https://qonto.com/en/legal-documents/privacy-notice for full details.

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