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Ingénieur MLOps — santé à l'échelle
Doctolib
Este anuncio está pensado para talento nativo en IA : habilidades y herramientas claras para saber si encajas antes de aplicar — y para reducir descartes por desajuste.
4 habilidades 2 herramientas
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Empleo
Modalidad Hybrid
Ubicación 54 Quai de la Rapée, Paris, IDF 75012, France · Hybrid
Compensación EUR 70k–95k/year + BSPCE Nivel de experiencia Beneficios Mutuelle, impact santé, hybrid Paris 12e. Cómo aplicar Feature store, CI/CD ML ou monitoring modèles HDS.
Publicada 29 dic 2026 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. 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.
Docker Tool
Containerized services and reproducible dev environments for ML and LLM workloads.
MLflow Tool
Regular use of MLflow in the ML platform & MLOps stack—configuration, debugging, and team conventions.
✓ 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. Lo que pide la empresa
Feature store, CI/CD ML ou monitoring modèles HDS.
Inicia sesión con cuenta candidato para aplicar
Regístrate, añade titular y bio; luego podrás adjuntar enlace o PDF de CV en Mi cuenta antes de aplicar. Así las candidaturas quedan ligadas a tu perfil.
Sugerencias
Talento IA que podría interesarte Perfiles ordenados por solape con habilidades y herramientas de este rol — útil si contratas equipo o comparas candidatos.
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Título
Ingénieur MLOps — santé à l'échelle
Aplicar Pensado para equipos que contratan personas que trabajan con IA, no solo alrededor.
Los candidatos ven habilidades y herramientas al inicio; recibes candidaturas estructuradas en un solo panel.
Llega a profesionales de IA que buscan roles con expectativas basadas en pruebas.
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