Agents Platform Engineer

Doctolib; Paris; Hybrid; EUR 110,000 - 170,000; Full time

Benefits offered by this job

Медицинская страховка для вас и детей

Копилка на питание (льготы)

Job summary

Doctolib ищет Senior Agents Platform Engineer для команды ML Platform. Ваша задача — построить платформу, позволяющую командам быстро разворачивать агентов с безопасной и соответствующей требованиям инфраструктурой в Европе.

Вы будете разрабатывать единый рантайм агентов, единую GenAI gateway и инфраструктуру наблюдения, обеспечения безопасности и аудита для обработки медицинских данных. Работа в гибридном формате в Париже, with удаленными днями по требованию.

Qualifications

• Опыт 5+ лет в разработке ПО, включая LLM-агентные системы.
• Опыт межсистемной интеграции и протоколов инструментов (MCP, A2A).
• Умение проектировать sandboxing, доступ и аудит данных.
• Свободное владение английским языком.
• Опыт работы с регулируемыми средами (GDPR, ISO 27001 и пр.).

Responsibilities

• Владение манифестом агента и реестром — спроектировать единую спецификацию.
• Управление GenAI gateway и маршрутизацией запросов на масштаб.
• Обеспечение надежности долгосрочных взаимодействий агентов и сред исполнения.
• Реализация филтров функций и эскалаций, аудит и безопасность данных.
• Обеспечение наблюдаемости и оценки качества агентов и процессов.
• Проведение валидации платформы на практике через демонстрацию референс-агентов.

Skills

Опыт разработки ПО

LLM-агентные системы

Дизайн агентов и памяти

Бездом временных окон/Checkpointing

АSОС SLO и инцидент-менеджмент

Английский язык

Tools

Kubernetes

Terraform

Datadog LLM Observability

MLflow

LiteLLM

Google ADK

Job description

Описание

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.

Задачи

• 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

Требования

• 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
• 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

Tech stack

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!

Условия

• 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

Собеседование

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

О компании

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

Диверситет и инклюзия

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!