Senior MLOps Engineer - Scale AI in Healthcare

Doctolib; Paris; Hybrid; EUR 90,000 - 140,000; Full time

Benefits offered by this job

Health insurance

Paid vacation

Mental health support

Remote work abroad

Lunch vouchers

Sports club subsidy

Transport subsidy

Parental leave support

DoctoGrowth plan

Disability accommodation

Relocation support

AI tools access

Job summary

Doctolib is seeking a Senior MLOps Engineer to join the ML Platform (TIGER) team in Paris. You will power ML, AI initiatives from experiment to production, building foundations for scalable, safe model deployment and monitoring across Europe.

You will work with data scientists and AI engineers to ensure reliability, performance, and observability of ML systems, contributing to Doctolib's AI-powered healthcare products.

Qualifications

• Experience as an MLOps or AI infrastructure engineer.
• Proficient in Python, Shell, and Terraform.

Responsibilities

• Partner with data scientists and ML/AI engineers to deploy models into production.
• Operate the orchestration layer for training, serving, and evaluation workloads on GCP/AWS with Kubernetes and Ray-based platforms.
• Build tools and best practices (Terraform modules, Kubernetes CRDs, GitOps) for safe, scalable deployments.
• Ensure availability and performance of ML systems; define and track SLOs; participate in on‑call and incident response.
• Maintain observability for ML systems using Datadog and Braintrust for evaluation signals.
• Collaborate on self-hosted LLM stack foundations (model registry, deployment automation).

Skills

Communication skills

Documentation skills

Tools

Kubernetes

Docker

Terraform

ArgoCD

GKE

Datadog

MLflow

Braintrust

Ray

Job description

Doctolib is seeking a Senior MLOps Engineer to join the ML Platform (TIGER) team in Paris. You will power ML, AI initiatives from experiment to production, building foundations for scalable, safe model deployment and monitoring across Europe.

You will work with data scientists and AI engineers to ensure reliability, performance, and observability of ML systems, contributing to Doctolib's AI-powered healthcare products.