The Role
Build and operate Doctolib’s foundational AI agent platform, including declarative agent registration, GenAI gateway routing, long-running agent execution, progressive rollouts, reliability, observability, evaluation, and compliance controls. The role owns production infrastructure serving healthcare applications, supports secure autonomous systems handling sensitive data, and advises feature teams on agent architecture. Responsibilities include on-call leadership, incident response, provider failover, reference-agent development, and platform integrations across Kubernetes, cloud providers, model services, and agent protocols.
Summary Generated by Built In
Set a new pulse for healthcare!
We are looking for a Senior Agents Platform Engineer to join the ML Platform (TIGER) team in AI & Machine Learning.
Your mission will be to build the platform that lets every feature team at Doctolib declare an agent once — and get a production-grade, secure, and compliant AI agent by default. You will work on the foundational layer that powers Consultation Assistant, Phone Assistant, Health Companion, and more: a shared agent runtime, a unified GenAI gateway, and the observability and evaluation infrastructure that makes it all trustworthy — contributing directly to safer, faster, and more reliable AI for patients and care teams 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:
- Own the agent manifest and registry — design the single declarative specification (skills, tools, memory, model, guardrails, sandbox, security) that wires any new agent into the full platform automatically, triggerable from product surfaces, other agents (A2A), schedules, or workflows
- Own the GenAI gateway end to end (built on LiteLLM) — manage routing (including KV-cache-aware routing), failover, metering, cost attribution, reserved-capacity pooling, and guardrails across self-hosted (vLLM) and managed providers, serving several hundred million requests per month
- Operate the infrastructure for long-running, streaming agent interactions — build production-grade reliability for SSE/WebSocket connections, queueing and retries, human-in-the-loop approvals, and snapshot/resume, in a sandboxed execution environment consistent with our existing application runtime
- Build feature-flagged, progressive rollout for prompts, models, and configs — including an emergency kill-switch and the audit trail that lets agents act safely on sensitive healthcare data
- Own reliability for the agents platform end to end — define SLOs, lead on-call and incident response, and build runbooks including provider failover for the GenAI gateway
- Build the observability and evaluation layer — online traces and logs, offline evals (LLM-as-judge plus patient and practitioner simulators) gating CI, and the compliance-grade audit trail for health data
- Validate the platform by shipping one or two reference agents yourself before product teams adopt it, and advise feature teams on agent architecture trade-offs
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 5+ years of software engineering experience, including hands-on production experience building or operating LLM-based agent systems (Google ADK or similar), with a solid understanding of agent design patterns: planning/reflection loops, tool calling, state and memory management, multi-agent coordination, and human-in-the-loop checkpoints
- Have experience with agent interoperability and tool protocols (MCP, A2A, AG-UI), control-plane concepts for asynchronous or long-running systems (queueing, retries, checkpointing), and feature-flagging or progressive-rollout systems including reliable kill-switches
- Have experience managing cost and capacity for LLM usage — routing, caching, and reserved-capacity planning with managed model providers
- Are comfortable owning production reliability (SLOs, incident response, on-call) and are security-minded when designing sandboxing, permission scoping, and audit trails for autonomous systems acting on sensitive data
- Have excellent communication and collaboration skills, high agency, a pragmatic build-vs-buy mindset, and are comfortable working in a founding role where shaping the right scope is part of the job
- Are fluent in English
It would be fantastic if you:
- Have experience giving agents richer identity, memory, and knowledge (long-term memory, artifact stores, or RAG pipelines), ideally in partnership with a data platform team, or experience building a feedback loop that turns production traces into evaluation datasets
- Have experience building Kubernetes-native abstractions (CRDs/operators) to manage an agent's lifecycle declaratively, and/or with durable stateful execution engines (e.g. Temporal) for long-running workflows
- Have experience in regulated environments (healthcare, finance, public sector) and with frameworks such as GDPR, HDS, ISO 27001, or SOC 2
- Programming languages: Python, plus comfort with TypeScript/Go for agent SDKs and tool integrations
- Cloud providers: GCP / AWS / Azure
- Agent orchestration & runtime: Google's Agent Development Kit (ADK), running as services on our application platform
- Agent protocols: MCP, A2A, and AG-UI
- Feature flags / progressive rollout: Flagd (or similar), including the emergency kill-switch
- GenAI gateway: LiteLLM (owned end-to-end by this team) — routing, failover, metering, cost attribution, guardrails, reserved-capacity pooling (Azure OpenAI PTUs, Vertex AI GSUs, AWS Bedrock Provisioned Throughput)
- Experiment tracking & model registry: MLflow
- LLM evaluation & observability: Braintrust and Datadog LLM Observability, plus distributed tracing for tool calls and agent decision paths
- Container / Orchestration: Kubernetes (GKE / EKS), Docker, Helm, Kustomize
- Infrastructure as code: Terraform
- Observability: Datadog
- And any other tool you deem relevant!
- 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 minutes)
- Hiring manager interview (1 hour)
- Case study & case restitution (1 hour)
- Behavioral interview (1 hour 30 minutes)
- 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, LiteLLM, Google ADK, 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
- 5+ years of software engineering experience
- Hands-on production experience building or operating LLM-based agent systems, such as Google ADK or similar
- Understanding of agent design patterns, including planning, reflection loops, tool calling, state and memory management, multi-agent coordination, and human-in-the-loop checkpoints
- Experience with MCP, A2A, AG-UI, asynchronous control planes, queueing, retries, checkpointing, feature flagging, progressive rollout, and kill-switches
- Experience managing LLM usage costs and capacity, including routing, caching, and reserved-capacity planning
- Experience owning production reliability, SLOs, incident response, and on-call operations
- Security-minded experience with sandboxing, permission scoping, and audit trails for autonomous systems handling sensitive data
- Excellent communication and collaboration skills
- Fluency in English
- Experience with long-term memory, artifact stores, RAG pipelines, or production-trace evaluation datasets
- Experience building Kubernetes-native abstractions such as CRDs or operators
- Experience with durable stateful execution engines such as Temporal
- Experience in regulated environments and frameworks such as GDPR, HDS, ISO 27001, or SOC 2
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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
