Senior ML Ops (x/f/m)

Job description

Set a new pulse for healthcare! We are looking for a Senior MLOps Engineer to join the ML Platform team. Your mission will be to build and scale the infrastructure that brings machine learning to life at Doctolib — powering AI-driven solutions that improve the daily experience of care teams and patients across Europe. You will work in a cross-functional team developing the ML platform and tooling that underpins Doctolib's AI products, contributing directly to faster, more reliable deployment of models that have a real impact on healthcare delivery. Working in the tech team at Doctolib means building innovative products and features to improve the daily lives of care teams and patients. What you'll do Your responsibilities include but are not limited to: Build and deploy production-grade machine learning models in close collaboration with data scientists and engineers, ensuring performance, scalability, and reliability Design and maintain the MLOps pipeline, including version control, CI/CD, and monitoring of ML models in production Develop tools, frameworks, and best practices to streamline the model development and deployment lifecycle Ensure the availability and performance of ML systems, proactively identifying and resolving issues before they impact users Partner with cross-functional teams to gather requirements, provide technical guidance, and contribute to the development of end-to-end ML solutions Share and advocate MLOps knowledge across the tech community, documenting processes, standards, and best practices to drive consistency and knowledge transfer Who you are Before you read on: if you don't have the exact profile described below, but you feel this job description matches your skill set, we still encourage you to apply. You'll be a great fit if you: Are proficient in Python, SQL, Shell Scripting, and Terraform, with hands-on experience building and containerizing ML pipelines with Docker Have solid knowledge of cloud platforms, particularly AWS services such as SageMaker, EC2, ECS, S3, and CloudWatch (and/or Azure equivalents) Have a good understanding of machine learning algorithms, concepts, and trends, including hands-on experience with Deep Learning frameworks — preferably PyTorch <li class="break-words text-fore