Staff Applied AI Engineer - Backend

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

Join us as a Staff Applied AI Engineer, Backend to build production systems in which AI is a core runtime capability. You'll help Qonto's Anti-Financial Crime teams investigate cases faster and with greater confidence by turning complex operational workflows into dependable, measurable tools.

➡️ 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

➡️ Your future team

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.

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