Staff Applied AI Engineer — Production AI Systems (Remote)

Qonto

Paris

Remote

EUR 120,000 - 180,000

Full time

5 days ago

Be an early applicant

Application generator

A complete application in a minute — tailored resume and cover letter, ready to send.

Get past ATS filters

Job summary

Qonto is seeking a Staff Applied AI Engineer, Backend to build production AI systems where AI is a core runtime capability. You will help AFC’s teams investigate cases faster by turning complex workflows into reliable automation.

You’ll join Qonto’s AI Compliance Tooling team in the Financial Crime Compliance domain, shaping architecture, collaborating with AFC stakeholders, and delivering measurable improvements in automation and reliability.

Qualifications

Production AI experience: shipped an AI agent or agentic workflow used by real users and can explain orchestration, tool use, state, outputs, evaluation, retries.
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 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 continuously learn.

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, 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.