The Role
Build and scale ML platform infrastructure, deploy production machine learning models, and maintain MLOps pipelines with version control, CI/CD, monitoring, and reliability practices. Develop tools and frameworks that streamline model deployment, collaborate with data scientists and engineers on end-to-end AI solutions, resolve production issues, and document and promote MLOps best practices across the technology organization.
Summary Generated by Built In
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 doYour 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.
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)
- 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
- 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
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 privacyAll 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.
Skills Required
- Proficiency in Python, SQL, Shell Scripting, and Terraform
- Hands-on experience building and containerizing machine learning pipelines with Docker
- Solid knowledge of cloud platforms, particularly AWS services such as SageMaker, EC2, ECS, S3, and CloudWatch, or Azure equivalents
- Understanding of machine learning algorithms, concepts, and trends
- Hands-on experience with deep learning frameworks, preferably PyTorch
- Excellent communication and collaboration skills
- Ability to produce clear technical documentation
- Fluency in English
- Experience with Kubernetes, GitOps tools such as ArgoCD, and/or Kafka
- Experience with ML model quantization and optimization
- Experience with Hugging Face Transformers, Accelerate, or PEFT
- Experience with JavaScript or TypeScript and browser-based model deployment using transformers.js or langchain.js
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The Company
What We Do
Since Doctolib's creation in 2013, we have had one purpose: strive for a healthier world. 1. We aim to improve the daily lives of care teams by providing them with a new generation of technologies and services. 2. We aim to improve health for all, by offering a fast and frictionless journey for all care episodes, creating new ways for people to receive care and empowering them to become actors of their health. At Doctolib, we are honored to work in the healthcare field and we believe that innovation in healthcare should be handled differently. We apply 4 guiding principles in everything we do: 1. We create helpful solutions for care teams and people. 2. We serve everyone equally and create well-designed and accessible technologies. 3. We team up with our users to strive for a healthier world and act as one team. 4. We protect our users' privacy. It’s their health, their data. To achieve our purpose, we are assembling a team dedicated to improving healthcare, with a human-centric approach and an entrepreneurial mindset. www.doctolib.com
