Staff Applied AI Engineer - Backend

LesJeudis; Paris; Remote; EUR 120,000 - 180,000; Full time

Job summary

Qonto is hiring a Staff Applied AI Engineer to build production AI systems and automated workflows within the Financial Crime Compliance domain. You will lead end-to-end projects, design robust backends, and establish measurable AI governance in a regulated fintech environment.

You’ll work with a cross-functional AFC team to ship agentic tooling, orchestrate models and tools, and ensure reliability, observability, and quality in production.

Qualifications

• Production AI experience: shipped AI agents or agentic workflows used by real users and explained orchestration, tools, state, outputs, retries, and failure modes.
• Strong backend engineering: solid system-design fundamentals across architecture, APIs, databases, integrations, reliability, observability, maintainability, and scalability.
• Staff-level autonomy and judgement: make sound decisions independently, communicate trade-offs clearly, and know when to optimize for speed vs. quality.
• Product and stakeholder thinking: turn ambiguous operational pain into a valuable solution without relying on a PM.
• End-to-end ownership: discover, build, ship, operate, maintain, and improve the systems you create.
• Learning agility: curious about AFC and regulated workflows; fintech/compliance helpful but not required.
• Pragmatic technology choices: Python plus familiarity with model providers or agent frameworks; production principles matter more than vendor knowledge

Responsibilities

• Build production AI systems: design and ship agentic tools and AI-powered workflows that gather context from multiple internal systems, orchestrate models and tools, parse structured outputs, and handle uncertainty and failure safely.
• Own projects end to end: lead discovery with AFC stakeholders, shape the solution, make architectural decisions, implement and launch it, then operate, maintain, and improve it in production.
• Design robust backend foundations: build reliable services, APIs, databases, and integrations around evolving AI models and tooling.
• Make AI behaviour measurable: define evaluation, observability, fallbacks, and human-review mechanisms; monitor output quality and operational impact.
• Deliver measurable operational value: reduce investigation lead time and expand automation across AFC workflows through incremental delivery.
• Shape the team's technical direction: lead design discussions, anticipate risks, balance speed with quality, raise the team's production capability.

Skills

Production AI

Backend engineering

Autonomy & judgement

Product thinking

End-to-end ownership

Learning agility

Pragmatic tech choices

Tools

Python

Agent frameworks

Job description

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 4.8 on Trustpilot,based on 55,000+ reviews. Our cultureputs customer satisfaction at the core of what we do, as proven by our Net Promoter Score of 75 (more about our culture here).

Our journey: Founded in 2017 byAlexandre and Steve, 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, of which 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 because the best teams are built on merit.

AI at Qonto: AI is deeply embedded in how we work (here) - 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.

Location: France, Germany, Spain, or Italy - remote within these hiring locations.

As a Staff Applied AI Engineer you will

• Build production AI systems: Design and ship agentic tools and AI-powered workflows that gather context from multiple internal systems, orchestrate models and tools, parse structured outputs, and handle uncertainty and failure safely
• Own projects end to end: Lead discovery with AFC stakeholders, shape the solution, make architectural decisions, implement and launch it, then operate, maintain, and improve it in production
• Design robust backend foundations: Build reliable, maintainable, and extensible services, APIs, databases, and integrations around evolving AI models and tooling
• Make AI behaviour measurable: Define evaluation approaches, observability, fallbacks, and human-review mechanisms; monitor output quality, acceptance and edit rates, throughput, and operational impact
• Deliver measurable operational value: Reduce investigation lead time and expand automation across AFC workflows through pragmatic, incremental delivery
• Shape the team's technical direction: Lead design discussions, anticipate risks, balance speed with quality, and help raise the team's capability in production agentic systems

What you can expect

• AI at the heart of the system: This is not a conventional backend role using AI only as a coding assistant, nor an ML research role. You'll build real products where model behaviour, orchestration, evaluation, and failure handling are production concerns
• High autonomy: There is no dedicated Product Manager. Engineers work directly with AFC stakeholders and own the path from an ambiguous operational need to a measurable production outcome
• Lean, iterative delivery: The team uses a daily 15-minute blocker sync, bi-weekly 1:1s, and lightweight tracking, leaving engineers focused on building and solving problems
• A close user feedback loop: You'll collaborate directly with operational teams and measure success through investigation lead-time reduction, output quality, human acceptance and edit rates, adoption, throughput, and resources saved
• A complex, meaningful domain: You'll learn how to build safe, scalable AI automation in a regulated environment where reliability and auditability matter

You'll join Qonto's AI Compliance Tooling team within the Financial Crime Compliance domain. The current team brings together Ioannis, the Tech Lead and a hands-on contributor; Staff Backend Engineer Enrique; Staff Machine Learning Engineer Luca; and Senior Backend Engineers Izan and Robson. The team is growing with two additional staff-level hybrid backend/AI hires.

Your future team

One important clarification: the team does not build fraud-detection engines or KYC/KYB rule engines. It consumes upstream signals and builds the AI-powered automation and intelligence layer used by human investigators. One current system gathers information from 10-15 internal tools and produces a structured compliance analysis for a person to approve or edit, reducing treatment time from approximately 15-20 minutes to about 30 seconds.

About you

• Production AI experience: You have shipped an AI agent or agentic workflow used by real users and can explain its orchestration, tool use, state, structured outputs, evaluation, retries, and failure modes
• Strong backend engineering: You bring solid system-design fundamentals across architecture, APIs, databases, integrations, reliability, observability, maintainability, and scalability
• Staff-level autonomy and judgement: You make sound decisions independently, communicate trade-offs clearly, and know when to optimise for speed and when quality is non-negotiable
• Product and stakeholder thinking: You can turn ambiguous operational pain into a valuable solution, challenge assumptions, prioritise scope, and define success without relying on a PM
• End-to-end ownership: You are willing to discover, build, ship, operate, maintain, and continuously improve the systems you create
• Learning agility: You are curious about AFC and regulated workflows and can ramp up quickly in a complex domain; prior fintech or compliance experience is helpful, not required
• Pragmatic technology choices: Python experience and familiarity with current model providers or agent frameworks are useful, but transferable production principles matter more than expertise in a specific language or vendor

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.

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On average, our hiring process lasts 20 working days. More information on our candidate journey here