Senior MLOps Engineer - Data Ingestion - Paris

Doctolib is hiring a Senior MLOps Engineer - Data Ingestion - Paris (France). Level rates it ; you can apply on Doctolib.

Level's read on this job

Builds AI ●●●● is how Level rates this role: the job is building AI systems.
Tools and skills the posting cites include MLflow, MLOps and AI evaluation.
Level classifies it as a senior role.
Level lists 155 other open roles at Doctolib.

AI in this role

mlflow

ml-opsai-evaluation

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.

What you'll bring

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

What we offer

Free comprehensive health insurance for you and your children
25 days of paid vacation per year, plus up to 14 days of RTT
Free mental health and coaching services through our partner Moka.care
Work from abroad for up to 10 days per year thanks to our flexibility days policy
Lunch vouchers (Swile card) worth €8.50 per working day, with €4.50 covered by Doctolib
A subsidy from the work council to refund part of the membership to a sport club or a creative class
50% reimbursement of your public transport subscription
Parent Care Program: receive one additional month of leave on top of the legal parental leave
For caregivers and workers with disabilities, a package including an adaptation of the remote policy, extra days off for medical reasons, and psychological support
Relocation support in case of international mobility
Access to the best AI tools for coding, development and dedicated training

Our interview process

Recruiter Interview
Technical Case Study Interview
System Design Interview
Behavioral Interview
At least one reference check

We want your experience to be clear, respectful, and transparent. Learn more about our hiring process on our candidate experience page.

Job details

Permanent position (CDI)
Tech stack: Python
Full-time
Nantes & Paris (Hybrid Policy: 2 days remote / week)
Start date: as soon as possible

We welcome everyone

At Doctolib, we are committed to improving access to healthcare for everyone. This translates into our recruitment process. We evaluate candidates based solely on qualifications and motivation, without any form of discrimination.

The more diverse ideas are heard, the more our product will truly improve healthcare for all. You are welcome to apply to Doctolib, regardless of your gender, religion, age, sexual orientation, ethnicity, or disability.

To ensure equal opportunities, we invite you to exclude personal information (e.g., pictures, age) from your applications. If you require any accommodation, please let us know for support during the hiring process.

Join us in building the healthcare we all dream of!

Your data privacy

All information provided is processed by Doctolib for application management. For data processing details, click here: France. Please contact hr.dataprivacy(at)doctolib.com for inquiries or to exercise your rights.

How we rate this

Senior MLOps Engineer - Data Ingestion - Paris at Doctolib rates 92 out of 100 for how much of the daily work is AI. That makes it Builds AI (AI Level 4 of 4). The level is about AI in the job, not seniority.

Classification

Builds AI. The job is building AI systems.

●●●● Builds AI80 to 100
●●●○ Works on AI60 to 79
●●○○ Uses AI40 to 59
●○○○ Little AI0 to 39

Levels come from how often the tools, models and workflows of the role are named in the posting itself. Read the description and count the AI tools, models and workflows it names.

Key facts

Senior MLOps Engineer - Data Ingestion - Paris is an open job at Doctolib.
Level rates this role AI Level 4 of 4: Builds AI.
The role is based in Paris, Paris, France.
The job was posted on April 7, 2026.
Seniority: senior.
Applications are handled by Doctolib. Apply on Doctolib

Prepare for this job

Built from this posting and from similar jobs on Level.

Skills and AI tools this role asks for

ML OpsAI EvaluationMLflow

Questions you could be asked

How do you monitor a model once it's live, and how do you know it needs retraining?
How do you decide that one model's output is better than another's for a given task?
What are the limits of MLflow that you've run into, and how did you work around them?
How would you decide a model or AI system is ready to ship?
Tell me about a time a model underperformed in production. How did you find out, and what did you change?

Adapt your resume

List these exact terms on your resume: ML Ops, AI Evaluation, and MLflow. An applicant tracking system matches the wording, not the idea.
Attach one line of real, concrete experience to at least one of them. A tool named with nothing behind it rarely survives a human read.
Lead with what you built, trained or shipped. This role is judged on the AI system itself, not the tools around it.

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