L'IA agentique d'entreprise.
À la française.
TL;DR: You build production-grade agents on the Idun Platform for our enterprise clients — not demos, not prototypes. You own the stack from architecture to eval to deployment. You are clear-headed about RAG vs fine-tuning trade-offs, you write eval suites before you call something done, and you can explain your technical decisions to a client Tech Lead in a 30-minute meeting. Paris-based, hybrid, €55–80k + equity.
Idun Group builds the operating foundation for enterprise AI agents. We deploy agents in production at large European enterprises using Idun Engine (open source, GPL 3.0) and Idun Platform (our proprietary control plane for governance, observability, and multi-agent orchestration). Our clients include organisations in aerospace, luxury goods, industrials, and financial services. We ship agents that process tens of thousands of requests per day under real production SLAs — not internal tools, not innovation-lab experiments.
There is a gap in the European AI market between the consultancies that can talk about agents and the teams that can actually build them to a production standard. Idun lives in the second group. As a Senior AI Engineer, you are the person building the agents that our clients deploy.
This is a builder role. You will take a scoped use case — a document QA agent, a multi-step compliance workflow, a customer-facing assistant — and take it from architecture through to a production deployment with observability, guardrails, access control, and an eval suite. You will work inside the client's environment, using the Idun Platform as your runtime, and hand off a system the client's team can operate without you.
The work is technically demanding. EU enterprises deploying agents under GDPR and the EU AI Act have real constraints: data must stay in-region, outputs must be auditable, access control must be policy-driven. These are solvable engineering problems, but they require someone who treats them as engineering problems rather than compliance footnotes.
You will also contribute back to the Idun Platform. When a client engagement surfaces a pattern that should be first-class in the runtime — a new memory backend, a guardrail primitive, a better eval harness — you write it up and ship it.
- Architect and build production agents using LangGraph, Google ADK, or Haystack on the Idun Platform — from a rough brief to a deployed, monitored, evaluated system. - Design the retrieval layer: chunk strategy, embedding models, index schema, query rewriting, re-ranking, eval loops. Make clear-headed RAG vs fine-tuning decisions and defend them with data. - Wire the full observability stack: OpenTelemetry spans to Langfuse or Phoenix, guardrails policies, audit log schemas, eval metrics. Production readiness means you can show an on-call engineer exactly what happened in any conversation. - Write eval suites before you declare a component done — unit tests for retrieval precision, integration tests for agent behaviour under adversarial prompts, latency benchmarks against production SLAs. - Participate in technical scoping with clients — translate a business problem into a component list, flag the hard unknowns early, set realistic delivery expectations. - Review client engineering teams' agent code during co-build phases; give structured feedback that improves the team's ability to maintain the system after handoff. - Feed roadmap-worthy patterns and abstractions back to the Idun Engine and Platform teams.