Senior ML Ops (x/f/m)

About the Company

Responsibilities

• ● 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.

Requirements

• ● 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 .

Benefits

• ● Free comprehensive health insurance (basic package) for you and your children
• ● 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
• ● % 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 .
• ● Permanent position