Senior MLOps Engineer for Healthcare AI Platform

Doctolib; Levallois-Perret; Hybrid; EUR 100,000 - 120,000; Full time

Benefits offered by this job

Health insurance

25 days vacation per year

Mental health support

Relocation assistance

Lunch vouchers

DoctoGrowth program

Transport subsidy

Job summary

Doctolib is seeking a Senior MLOps Engineer to join the ML Platform team in Paris. You will build and scale infrastructure that brings machine learning to life, powering AI-driven features for care teams and patients across Europe.

You will own the MLOps pipelines, work with data scientists and software engineers to deploy robust, scalable models, and contribute to tooling, monitoring, and best practices in a hybrid, collaborative environment.

Qualifications

• Strong programming experience in Python for ML systems.
• Hands-on experience building and containerizing ML pipelines.
• Experience with cloud platforms and ML model deployment tooling.

Responsibilities

• Build and deploy production-grade ML models with collaboration from data scientists and engineers.
• Design and maintain the MLOps pipeline with version control, CI/CD, and monitoring.
• Develop tools and best practices to streamline model development and deployment.
• Ensure availability and performance of ML systems, proactively solving issues.
• Partner with cross-functional teams to deliver end-to-end ML solutions.
• Share MLOps knowledge and document processes for consistency.

Skills

Python

SQL

Shell Scripting

Terraform

Docker

Cloud platforms

PyTorch

HuggingFace transformers

Kubernetes

GitOps (ArgoCD)

Kafka

Tools

AWS SageMaker

EC2

ECS

S3

CloudWatch

Docker

Kubernetes

ArgoCD

LangChain.js

Transformers

Job description

Doctolib is seeking a Senior MLOps Engineer to join the ML Platform team in Paris. You will build and scale infrastructure that brings machine learning to life, powering AI-driven features for care teams and patients across Europe.

You will own the MLOps pipelines, work with data scientists and software engineers to deploy robust, scalable models, and contribute to tooling, monitoring, and best practices in a hybrid, collaborative environment.