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Employment: Full-time · Permanent — employment source
Job details Permanent position Tech stack: Python / Terraform / Kubernetes
Tech stack: Python / Terraform / Kubernetes Full-time Paris, France
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What you’ll work on

Partner with data scientists, ML & AI engineers to take models from experimentation to reliable, scalable production deployment.

From the employer’s posting

Your responsibilities include but are not limited to: Partner with data scientists, ML & AI engineers to take models from experimentation to reliable, scalable production deployment. Operate and evolve the orchestration layer for training, serving and evaluation workloads, built on GCP and AWS using Kubernetes (GKE) and Ray-based platforms (Anyscale) — including CI/CD, versioning and automated testing pipelines that carry a model from the registry to a served endpoint.

Tools in this posting

Java
Kotlin
Python
TypeScript
AWS
Azure
BigQuery
Datadog
Google Cloud (GCP)
Kubernetes
MLflow
Terraform
React
Docker

Source

- 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.

- Have prior experience as an MLOps Engineer, Cloud Engineer for ML applications, AI Infrastructure Engineer, or Platform Engineer for AI, or in a similar role - Are proficient in Python, Shell scripting and Terraform, with a solid understanding of machine learning algorithms, concepts and trends - Have hands-on experience with Kubernetes in production and GitOps tools (e.g. ArgoCD), and have built MLOps pipelines for containerizing models and solutions with Docker

- Permanent position - Tech stack: Python / Terraform / Kubernetes - Full-time

- Partner with data scientists, ML & AI engineers to take models from experimentation to reliable, scalable production deployment. - Operate and evolve the orchestration layer for training, serving and evaluation workloads, built on GCP and AWS using Kubernetes (GKE) and Ray-based platforms (Anyscale) — including CI/CD, versioning and automated testing pipelines that carry a model from the registry to a served endpoint. - Build the tools, templates and best practices (Terraform modules, Kubernetes CRDs and operators, GitOps workflows, internal libraries) that let any ML team deploy and operate their models and agents safely and consistently.

- Have hands-on experience with Kubernetes in production and GitOps tools (e.g. ArgoCD), and have built MLOps pipelines for containerizing models and solutions with Docker - Have experience with cloud platforms such as GCP (GKE, BigQuery) and/or AWS or Azure equivalents, and with observability tools such as Datadog - Have hands-on experience with an experiment tracking/model registry tool (e.g. MLflow) and an LLM evaluation/observability tool (e.g. Braintrust)

- Ensure the availability, reliability and performance of ML systems in production across training, serving and the agents platform; define and track SLOs; take part in on-call and incident response for the whole stack. - Build and maintain observability for ML systems: infrastructure and model health via Datadog, and evaluation/quality signals via Braintrust, catching regressions before they reach users. - Work hand in hand with AI inference engineers on the shared foundations of our self-hosted LLM stack (model registry, deployment automation, shared GPU capacity), and stay current with MLOps practices for classic ML, generative AI and agentic systems alike.

- Operate and evolve the orchestration layer for training, serving and evaluation workloads, built on GCP and AWS using Kubernetes (GKE) and Ray-based platforms (Anyscale) — including CI/CD, versioning and automated testing pipelines that carry a model from the registry to a served endpoint. - Build the tools, templates and best practices (Terraform modules, Kubernetes CRDs and operators, GitOps workflows, internal libraries) that let any ML team deploy and operate their models and agents safely and consistently. - Ensure the availability, reliability and performance of ML systems in production across training, serving and the agents platform; define and track SLOs; take part in on-call and incident response for the whole stack.

- Are proficient in Python, Shell scripting and Terraform, with a solid understanding of machine learning algorithms, concepts and trends - Have hands-on experience with Kubernetes in production and GitOps tools (e.g. ArgoCD), and have built MLOps pipelines for containerizing models and solutions with Docker - Have experience with cloud platforms such as GCP (GKE, BigQuery) and/or AWS or Azure equivalents, and with observability tools such as Datadog

It would be fantastic if you: - Have hands-on experience with Kubernetes-native agent abstractions (CRDs/operators, e.g. kagent) or building production agent runtimes - Have experience with ML model quantization and optimization

- Have experience with cloud platforms such as GCP (GKE, BigQuery) and/or AWS or Azure equivalents, and with observability tools such as Datadog - Have hands-on experience with an experiment tracking/model registry tool (e.g. MLflow) and an LLM evaluation/observability tool (e.g. Braintrust) - Are a strong team player with excellent communication and documentation skills, comfortable with an evolving scope, and pragmatic about build vs. buy decisions

Benefits in the posting

Full benefits wording

Free comprehensive health insurance (basic package) 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
ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave and 75% during the second, covered by Doctolib
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
Our interview process
At least one reference check
Job details
Permanent position
Full-time
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

From the employer’s posting.

Job description

View original posting ↗

Set a new pulse for healthcare!

We are looking for a Senior MLOps Engineer to join the ML Platform (TIGER) team in Artificial Intelligence & Machine Learning.

Your mission will be to power every ML and AI initiative at Doctolib — from experiment to reliable production service. You will build and operate the foundations that let data scientists, ML engineers, and AI teams ship models and agents safely, fast, and at scale, from classic machine learning to self-hosted generative AI. You will contribute directly to the reliability and performance of AI-powered features that improve the daily lives of millions of patients and thousands of healthcare professionals across Europe.

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:

Partner with data scientists, ML & AI engineers to take models from experimentation to reliable, scalable production deployment.
Operate and evolve the orchestration layer for training, serving and evaluation workloads, built on GCP and AWS using Kubernetes (GKE) and Ray-based platforms (Anyscale) — including CI/CD, versioning and automated testing pipelines that carry a model from the registry to a served endpoint.
Build the tools, templates and best practices (Terraform modules, Kubernetes CRDs and operators, GitOps workflows, internal libraries) that let any ML team deploy and operate their models and agents safely and consistently.
Ensure the availability, reliability and performance of ML systems in production across training, serving and the agents platform; define and track SLOs; take part in on-call and incident response for the whole stack.
Build and maintain observability for ML systems: infrastructure and model health via Datadog, and evaluation/quality signals via Braintrust, catching regressions before they reach users.
Work hand in hand with AI inference engineers on the shared foundations of our self-hosted LLM stack (model registry, deployment automation, shared GPU capacity), and stay current with MLOps practices for classic ML, generative AI and agentic systems alike.

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:

Have prior experience as an MLOps Engineer, Cloud Engineer for ML applications, AI Infrastructure Engineer, or Platform Engineer for AI, or in a similar role
Are proficient in Python, Shell scripting and Terraform, with a solid understanding of machine learning algorithms, concepts and trends
Have hands-on experience with Kubernetes in production and GitOps tools (e.g. ArgoCD), and have built MLOps pipelines for containerizing models and solutions with Docker
Have experience with cloud platforms such as GCP (GKE, BigQuery) and/or AWS or Azure equivalents, and with observability tools such as Datadog
Have hands-on experience with an experiment tracking/model registry tool (e.g. MLflow) and an LLM evaluation/observability tool (e.g. Braintrust)
Are a strong team player with excellent communication and documentation skills, comfortable with an evolving scope, and pragmatic about build vs. buy decisions

It would be fantastic if you:

Have hands-on experience with Kubernetes-native agent abstractions (CRDs/operators, e.g. kagent) or building production agent runtimes
Have experience with ML model quantization and optimization
Have experience with GPU sharing/partitioning technologies (e.g. NVIDIA MIG, time-slicing, MPS)

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 we offer

Free comprehensive health insurance (basic package) 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
ParentCare Program: Enjoy full salary coverage (100%) during your first month of birth leave and 75% during the second, covered by Doctolib
Enrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth
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

HR Interview by phone (45 min)
Hiring Manager Interview (1 hour)
Case Study & Restitution: MLOps pipeline design and AI agent production-acceleration exercise (1h30)
Behavioral Interview (1h30)
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
Tech stack: Python / Terraform / Kubernetes
Full-time
Paris, France
Hybrid work setup (up to 2 remote days per 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.

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Pay: No pay amount identified in the saved description.
Location & working pattern: Paris, Paris, France
- Enrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth - 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
More source context
- Paris, France - Hybrid work setup (up to 2 remote days per week) - Start date: as soon as possible
Work authorization: No clear work-authorization passage found. Eligibility is unconfirmed.

Open source at job-boards.greenhouse.io ↗Read the full description ↓

Status in our records: Active
First seen by us: Oct 2, 2026
Recorded sightings: 10
Last seen by us: Oct 9, 2026

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Senior ML Ops (x/f/m)