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AI role · Open offer

ML Engineer — fraud detection

Qonto

Permanent (CDI)
Full-time
Senior
Risk ML
Hybrid
18 Rue de Navarin, Paris, IDF 75009, France · Hybrid
Paris, France
EUR 75k-100k/year + BSPCE

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At a glance

How you'll work, where, and key listing dates.

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: 18 Rue de Navarin, Paris, IDF 75009, France · Hybrid
Compensation: EUR 75k-100k/year + BSPCE
Experience level: Senior
Team: Risk ML
Benefits & perks: BSPCE, mutuelle, remote flexible.
How to apply: Modèle fraude temps réel ou graph ML.
Company site: qonto.com
Listed: Jun 27, 2026
Updated: Aug 3, 2026

About this role

What the team is looking for and how this position fits.

Qonto is hiring a Senior ML Engineer — fraud detection on the Risk ML team (Hybrid · Paris, France). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Qonto est la banque pro des PME en Europe. Le ML détecte fraude, optimise onboarding et automatise le support.

ML Engineer — fraude (Paris) : scoring temps réel, graph features, et explicabilité réglementaire.

What you will do

Ship production fintech fraud ML with clear evaluation, monitoring, and rollback paths—not demo-only notebooks.
Partner with product, design, and domain experts on requirements, trade-offs, and safe rollout.
Improve data quality, labeling, and offline metrics that correlate with business KPIs.
Document architectures and runbooks for on-call and customer-facing teams.
Mentor engineers on testing, observability, and responsible AI practices.

What we look for

Strong Python and modern ML/LLM stack experience with multiple shipped features.
Comfort with SQL, experimentation, and communicating results to non-technical stakeholders.
Pragmatic ownership from prototype to production—including cost and latency budgets.
Eligible to work in the listed country; professional English (French a plus for Paris roles).

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
PyTorch
Training and inference with PyTorch: reproducible experiments, checkpointing, and deployment-minded exports.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

KafkaTool
Streaming features or scores with ordering, idempotency, and monitoring for real-time systems.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Modèle fraude temps réel ou graph ML.
Several of these show up in recent shipped work—not only on your CV: Python, PyTorch, SQL, and Kafka.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Paris, France)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.
You read the full listing (comp: EUR 75k-100k/year + BSPCE, benefits in At a glance) and your expectations align.

Apply

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What the employer asked for

Modèle fraude temps réel ou graph ML.

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Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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