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Ingénieur MLOps santé | Doctolib | Paris · Ganloss

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AI role · Open offer

Ingénieur MLOps — santé à l'échelle

Doctolib

Permanent (CDI)
Full-time
Senior
ML platform
Hybrid
54 Quai de la Rapée, Paris, IDF 75012, France · Hybrid
Paris, France
EUR 70k–95k/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.

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At a glance

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

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: 54 Quai de la Rapée, Paris, IDF 75012, France · Hybrid
Compensation: EUR 70k–95k/year + BSPCE
Experience level: Senior
Team: ML platform
Benefits & perks: Mutuelle, impact santé, hybrid Paris 12e.
How to apply: Feature store, CI/CD ML ou monitoring modèles HDS.
Company site: www.doctolib.fr
Listed: Jun 22, 2026
Updated: Aug 3, 2026

About this role

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

Doctolib is hiring a Senior Ingénieur MLOps — santé à l'échelle on the ML 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.

Doctolib est la plateforme santé leader en Europe — des millions de rendez-vous et des copilots cliniques soumis à HDS et RGPD.

Ingénieur MLOps (hybride Paris 12e) : industrialiser modèles ML santé — feature store, CI/CD ML, monitoring et déploiements multi-pays.

À propos de Doctolib

Scale-up française ; impact direct sur parcours patient et praticien.
Équipes IA à Paris, Berlin et remote Europe.
Exigences fortes sur confidentialité et traçabilité modèles.

Missions

Standardiser pipelines entraînement et promotion modèles.
Mettre en place alerting dérive et dashboards latence/coût.
Collaborer avec data engineers sur feature store GCP.

Stack & environnement

Python, Kubernetes, MLflow, GCP, BigQuery, Terraform.
Hybride Paris 12e ; français courant.

Profil recherché

4+ ans MLOps ou platform ML en environnement régulé.
Éligibilité travail en France.

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.
Kubernetes
Deploying and scaling model services with health checks, autoscaling, and rollout discipline.
SQL
Analytical SQL for features, cohorts, label quality, and experiment analysis at scale.
Machine Learning
Practical Machine Learning experience applied to training, deployment, observability, and cost control in production or rigorous pilots.

Tools & stack

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

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
MLflowTool
Regular use of MLflow in the ML platform & MLOps stack—configuration, debugging, and team conventions.

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: Feature store, CI/CD ML ou monitoring modèles HDS.
Several of these show up in recent shipped work—not only on your CV: Python, Kubernetes, SQL, Machine Learning, and Docker.
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 70k–95k/year + BSPCE, benefits in At a glance) and your expectations align.

Apply

Candidate accounts only: add your headline and bio on My account, 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

Feature store, CI/CD ML ou monitoring modèles HDS.

Sign in with a candidate account to apply

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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Ingénieur MLOps — santé à l'échelle

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