Senior MLOps Engineer - Data Ingestion - Paris
About this role
Your Impact
We are looking for a Senior MLOps Engineer to join the Panda Team (Data & ML Operations) in Data & AI Platform team.
Your mission will be to build and maintain secure ML pipelines in production, transforming how we handle healthcare data at scale. You will work in a feature team developing critical data infrastructure that enables data-driven decision-making while protecting patient privacy across millions of users.
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 build
Your responsibilities include but are not limited to:
Design and implement end-to-end ML model pipelines in production (LLM and custom models) with robust deployment, evaluation, and monitoring frameworks
Own data pseudo-anonymization architecture within ingestion services, converting Tier 0 (personal identifiers) to Tier 1 (anonymized data) while ensuring data quality and model performance
Build and maintain secure data export services with ML-based threat detection to prevent attack vectors (SQL injection, etc.) using adaptive models rather than manual rules
Manage golden datasets and implement production model evaluation frameworks to ensure anonymization quality and system reliability
Build and maintain data pipelines that efficiently extract, transform, and load data from various sources, handling multiple data formats (text, images, audio, video)
Implement automation and orchestration tools using ML orchestration platforms (MLflow, Braintrust, or similar) to streamline infrastructure provisioning and reduce manual effort
Monitor data and ML platforms for performance, reliability, and security; identify and troubleshoot issues proactively
Mentor team members on MLOps expertise and best practices to reduce knowledge silos and build organizational capability
Life at Doctolib Tech
Our solutions are built on a single fully cloud-native platform that supports web and mobile app interfaces, multiple languages, and is adapted to country and healthcare specialty requirements.
Our stack is composed of Rails, TypeScript, Java, Python, Kotlin, Swift, and React Native.
We leverage AI ethically across our products to empower patients and health professionals. Discover our AI vision here.
Want to learn more about our tech culture and environment? Visit the Doctolib Tech site.
Requirements
- 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:
- You have at least 7+ years as an MLOps Engineer or ML Platform Engineer with proven production model lifecycle management experience
- You have expert-level experience with ML orchestration tools (MLflow, Braintrust, or similar) for batch processing and inference pipelines
- You have a strong Site Reliability Engineering (SRE) foundation with focus on operations excellence, reliability, and observability
- You have expertise in Python for automation and ML pipeline scripting
- You have strong proficiency with infrastructure-as-code tools such as Terraform and container orchestration (Kubernetes)
- You have experience with model evaluation frameworks and golden dataset management
- You have a solid understanding of cloud infrastructure (preferably GCP, AWS, or Azure)
- You have excellent problem-solving skills with focus on identifying and resolving infrastructure bottlenecks
- You are fluent in English
- It would be fantastic if you:
- Have production LLM or custom model deployment experience
- Have knowledge of data security and privacy frameworks (GDPR, data anonymization, pseudonymization)
- Have experience building and monitoring security services and threat detection systems
- Have strong communication and mentoring skills to drive knowledge transfer across teams