AI role · Open offer

GenAI Consultant — enterprise delivery

Capgemini Invent

This listing is optimized for AI-native talent: clear skills and tools so candidates know if they fit before they apply—and so you spend less time screening mismatches.

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At a glance

How you'll work, where, and key listing dates.

Employment: Permanent (CDI), Full-time
Workplace: Hybrid
Location: 15 Rue du Linguel, Strasbourg, GES 67000, France · Hybrid
Compensation: EUR 48k–68k/year + variable
Experience level: Mid
Team: GenAI practice
Benefits & perks

About this role

What the team is looking for and how this position fits.

Capgemini Invent is hiring a Mid GenAI Consultant — enterprise delivery on the GenAI practice team (Hybrid · Strasbourg, France). This is a hands-on role in LLM & generative AI—not a generic “AI enthusiast” post.

You will own LLM features, retrieval, agents, and evaluation end to end: problem framing, offline evaluation, safe rollout, and iteration from production signals. Success is measured with clear metrics (quality, latency, cost, or business KPIs)—not slide decks alone.

Capgemini Invent depuis Strasbourg : GenAI grands comptes.

GenAI Consultant : RAG, agents, gouvernance, handover MLOps.

Missions

POC RAG/agents avec evals et ROI.
Transfert compétences client.

Stack & environnement

Python, LangChain, Azure, Docker.

Profil recherché

Consulting + delivery ML/LLM.

Skills we're hiring for

What each capability means for this role—not a keyword checklist.

Python
Production Python for data pipelines, model code, APIs, and automated tests—not notebook-only workflows.
LangChain
Orchestrating LLM chains, tools, and retrieval with observable steps and failure handling.
OpenAI API

Tools & stack

Platforms and products mentioned for this role (APIs, IDEs, vector DBs, CRMs, design tools, etc.).

DockerTool
Containerized services and reproducible dev environments for ML and LLM workloads.
AzureTool
Regular use of Azure in the LLM & generative AI stack—configuration, debugging, and team conventions.

You're a strong fit if…

Tailored to this listing—use it as a self-check before you apply.

You can show evidence in your application: Cas client RAG ou agents avec gouvernance et handover MLOps.
Several of these show up in recent shipped work—not only on your CV: Python, LangChain, OpenAI API, MLOps, and Docker.
You have owned LLM features, retrieval, agents, and evaluation in production: debugging live issues, running postmortems, and iterating from real user or business signals.
You are comfortable with the hybrid rhythm (Hybrid · Strasbourg, France)—onsite collaboration when it matters, deep work when remote.
You explain trade-offs (quality, cost, latency, safety) in plain language—with numbers or examples, not buzzwords.

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What the employer asked for

Cas client RAG ou agents avec gouvernance et handover MLOps.

Register, add a headline and bio, then you can attach a CV link or PDF on My account before you apply. That keeps applications tied to your profile and makes it easier for employers to review your background.

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