AI role · Open offer

MLOps Engineer — model CI & monitoring

Qonto

Permanent (CDI)
Full-time
Senior
Data platform
Hybrid
Paris, Île-de-France, France · Hybrid
Paris, France
EUR 62k–85k/year + BSPCE

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

4 skills3 tools

At a glance

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

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: Paris, Île-de-France, France · Hybrid
Compensation: EUR 62k–85k/year + BSPCE
Experience level: Senior
Team: Data platform
Benefits & perks

About this role

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

Qonto is hiring a Senior MLOps Engineer — model CI & monitoring on the Data platform team (Hybrid · Paris, France). This is a hands-on role in ML platform & MLOps—not a generic “AI enthusiast” post.

You will own training, deployment, observability, and cost control 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 néobanque des PME européennes — data & ML à Paris.

MLOps Engineer: CI modèles, monitoring de drift, et maîtrise des coûts GPU.

Missions

Industrialiser entraînement et serving avec pipelines reproductibles.
Alerter sur drift / perf et piloter le coût d'inférence.

Stack & environment

Python, Kubernetes, Airflow, Docker, SQL.

What we look for

MLOps ou platform ML en fintech; hybrid Paris.

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.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.
Machine Learning

Tools & stack

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

KubernetesTool
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.

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: CI for models, drift alerts, or GPU cost dashboards.
Several of these show up in recent shipped work—not only on your CV: Python, SQL, Machine Learning, MLOps, and Kubernetes.
You have owned training, deployment, observability, and cost control 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 62k–85k/year + BSPCE, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on , and optionally a CV link or PDF so recruiters see your full résumé. Then write a short pitch below—applications use your login email.

What the employer asked for

CI for models, drift alerts, or GPU cost dashboards.

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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