Senior MLOps Engineer - Data Ingestion - Paris

Benefits

Parental leave

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Non-birth-parent leave

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Family-building benefits

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• Surrogacy assistance: Not verified

Mental health support

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Relocation assistance

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Market context

U.S. role benchmark (BLS OEWS)

$116,543 U.S. median for this role

Projected growth (BLS Employment Projections)

+9.8% - Much faster than average

Matched to SOC 15-1252 - Software Engineering aggregate by role bucket.

Source: U.S. Bureau of Labor Statistics, OEWS, May 2024 and Employment Projections, 2024-2034.

Role

Role function

Engineering From the posting source checked Jun 20, 2026

Seniority

Senior From the posting source checked Jun 20, 2026

Schedule

Shift type

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Weekend work

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Application

Cover letter

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Assessment

Required From the posting source checked Jun 20, 2026

Deadline

Not stated

Where they hire

State eligibility is not yet verified.

About this role

Senior MLOps Engineer - Data Ingestion - Paris Paris, Paris, France 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

Read the full description at job-boards.greenhouse.io. FewerJobs shows a preview and links to the original posting.