Senior Agents Platform Engineer (x/f/m)

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 do

Your 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

…