About this role
A Senior MLOps Engineer at Doctolib will play a key role in establishing and scaling the infrastructure that enables AI-driven solutions across the company. This individual will be integral to the ML Platform team, focusing on the engineering required to bring machine learning models into reliable production environments. The work involves developing and maintaining the core tooling and pipelines necessary for the deployment of models that directly enhance the experience for care teams and patients throughout Europe. This role exists to ensure that the AI initiatives are deployed quickly, reliably, and with high performance, directly impacting healthcare delivery. Candidates who thrive in a cross-functional setting, possess a proactive approach to problem-solving, and are dedicated to sharing knowledge would find this position suitable.
The successful candidate will collaborate closely with data scientists and other engineers to build robust ML systems. They will be responsible for designing the MLOps pipeline, implementing CI/CD processes, and establishing comprehensive monitoring for models in production. Furthermore, this role requires advocating for best practices and documenting processes to foster consistency across the ML development lifecycle. It is ideal for someone who enjoys bridging the gap between model development and robust, scalable production engineering.
This position is for an engineer who is comfortable operating in a cloud-native environment and has a genuine interest in applying ML to solve real-world healthcare challenges. The ideal candidate is someone who can take ownership of the entire model deployment lifecycle, from development to monitoring, ensuring that the AI features delivered to users are both performant and dependable.
What you will do
- Build and deploy production-grade machine learning models in collaboration with data scientists and engineers.
- Design and maintain the MLOps pipeline, encompassing version control, CI/CD, and monitoring for ML models in production.
- Develop tools, frameworks, and best practices aimed at streamlining the model development and deployment lifecycle.
- Ensure the availability and performance of ML systems by proactively identifying and resolving potential issues before they affect users.
- Partner with cross-functional teams to gather requirements and provide technical guidance for end-to-end ML solutions.
- Share and advocate MLOps knowledge by documenting processes, standards, and best practices to promote knowledge transfer.
What you need
- Proficiency in Python, SQL, Shell Scripting, and Terraform is required for the role.
- The candidate must have hands-on experience building and containerizing ML pipelines using Docker.
- Solid knowledge of cloud platforms, specifically AWS services such as SageMaker, EC2, ECS, S3, and CloudWatch, is necessary.
- A good understanding of machine learning algorithms, concepts, and trends, including practical experience with Deep Learning frameworks like PyTorch is needed.
- Excellent communication and collaboration skills are essential for working effectively within cross-functional teams.
- The candidate should possess a strong team spirit, a proactive sense of initiative, and a genuine enthusiasm for learning.
- Fluency in English is required to communicate effectively within the team.
Nice to have
- Experience with Kubernetes
- Experience with GitOps tools (e.g., ArgoCD)
- Experience with Kafka
- Experience with ML model quantization, optimization, and HuggingFace technologies (Transformers, Accelerate, PEFT)
- Experience with JavaScript/TypeScript and browser-based model deployment (transformers.js / langchain.js)
Benefits
- Free comprehensive health insurance (basic package) for you and your children
- 25 days of paid vacation per year
- Up to 14 days of RTT
- Free mental health and coaching services through their partner Moka.care
- Work from abroad for up to 10 days per year
- 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: Full salary coverage (100%) during the first month of birth leave and 75% during the second
- Enrollment in Doctolib's long-term employee value sharing plan called DoctoGrowth for caregivers and workers with disabilities, 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
Skills and tech stack
Questions candidates ask
What is the remote policy?
The policy allows employees to work from abroad for up to 10 days per year due to flexibility days policy.
What is the compensation structure?
The posting does not specify the salary for this role.
What is the primary technology stack mentioned?
The core tech stack includes Python, PySpark, SQL, AWS SageMaker, Terraform, and Docker.
What is the team like?
The role is within the ML Platform team, working in a cross-functional team alongside data scientists and engineers.
What is the hiring process like?
The interview process includes a Recruiter Interview, a Hiring Manager Interview, a Case Study & Case Restitution, a Behavioral Interview / Meet the Team session, and at least one reference check.