CDI - Senior AI Engineer - Paris

Mission

New models, whether open or closed, are released every month: it is still necessary to choose those that are good enough for production, and to demonstrate it. Applications and agents rely on large language models (LLMs), and the model part of these systems is in your hands. Our clients use them to respond to their own clients, process documents, and query their knowledge.

What the model sees, how it retrieves knowledge, the tools it can call: these choices are yours, as is the measurement of response quality. The chosen model must meet cost and quality objectives.

This position is for you if you want to know if an AI system gives correct answers, and not just if it runs.

Artefact accompanies the world's largest companies in the deployment of AI: 3,000 AI makers in over 30 countries, more than 1,000 clients. In France, the Engineering department brings together around 300 people under the responsibility of the CTO; you will join the AI Applied Engineer chapter, the AI Engineer archetype.

Activities and Responsibilities

Product Engineers build the application around the model; the model's behavior within it is your responsibility. Context & Analytics Engineers prepare the knowledge that your agents retrieve, and Platform Engineers leverage the serving, harness, and observability on which you rely.

With the AI Product Manager and the client's business experts, a good answer is defined, then the results are examined together. Peer code reviews and mentoring new hires are part of the daily routine.

Your responsibilities:

• Build LLM applications and agents: context design, tool calling, multi-step workflows.
• Design how agents leverage knowledge: retrieval queries, re-ranking, citations.
• Build evaluation datasets and pipelines: reference answers, automated scoring, review by business experts.
• Monitor the quality of responses in production and correct any degradation.
• Choose models based on quality, cost, and latency; perform fine-tuning or distillation on a smaller model when prompting is not enough.
• Add safeguards against bad answers, data leaks, and prompt injection.
• Process documents with LLMs: extraction, classification, summarization.

Candidate Profile

The Essentials:

1. You have built an application or an agent that calls an LLM, and you can explain what worked and what failed.
2. Your code is tested and maintainable.
3. Coding agents are already part of your daily work, and you verify what they produce.
4. The quality of AI outputs is measured by you with test sets and metrics, and you know how to limit hallucinations, prompt injection, and data leaks.
5. Deploying an LLM application into production and maintaining it (deployment, monitoring, cost, latency) holds no secrets for you.
6. The starting point for you is the business problem rather than the model; recognizing that AI is not always the right tool, and explaining it to non-technical people, is part of your practice.

A plus, not a prerequisite:

• Fine-tuning or distillation of language models.
• A degree in machine learning or statistics, for example in data science.
• Agent frameworks or protocols, such as MCP.
• Multimodal inputs: documents, images, voice.
• LLM observability tools: tracing, cost tracking.

You don't need to tick all the boxes to apply: if you have most of the essentials and the desire to learn the rest, let's talk.

Why join us?

In this field, the difficulty is less about making a model speak than knowing if it speaks the truth. The AI Engineer answers this question through measurement: test sets, evaluations, production monitoring, rather than the impression of a successful demonstration.

We are AI makers: AI is built with our clients, not just for them. Their challenges become ours, and our technical choices are explained in the language of business. Our positions are clear; they evolve in the face of facts, never under pressure.

Our field changes every month, and this pace suits us: new technologies and sectors are learned quickly, and habits that no longer serve are abandoned without regret. Faced with a difficult problem, the reflex is to break it down into verifiable steps, with a high level of demand on both code and deliverables.

When something is missing, for a client or for the chapter, we build it. Progression comes through constructive feedback, knowledge sharing, asking for help without delay, and recognizing the work of others. There is much to learn, and much to build.

A counselor accompanies you through your first months. Technical onboarding opens the journey, followed by a first client project; if none are ready, internal starter kits take over.

The rest is deepened on projects: evaluation, agent design, model adaptation, with the support of the AI Engineer community. From 2027 onwards, the AI Engineer archetype will have its own skills framework and training path, in addition to the common foundation for all AI Applied Engineers (production responsibility, AI security basics).

Later, a transverse role becomes possible (Project Tech Lead, Pre-sales Engineer, Engineering Manager), as do the AI Architect and AI Product Manager chapters.

Recruitment Process

The process takes approximately four hours, spread over six stages, with only one on-site.

1. HR Interview (45 min, remote): background, motivations, and practical points, such as schedule and salary expectations.
2. Call with the CTO (15 to 30 min, remote).
3. Technical Screening (30 min, remote, with a member of the AI Applied Engineer chapter): detailed review of your past projects, followed by technical and solution design questions.
4. Code Review and Live Coding (60 min, on-site, with a chapter member): review of an existing code repository and correction of its defects, then construction of a new feature with a coding agent and presentation of the result.
5. Technical Case Study (45 min, remote, with a chapter member): framing a business problem, proposing a technical solution, describing the evaluation method, then discussing your choices.
6. Final Interview (30 min, remote, with a member of the department's management, Engineering Manager or Director): review of any outstanding points, selection of the archetype and level best suited to your profile, and answers to all your questions.

Depending on the stages, all AI tools are authorized, or none are. During live coding, the way of working with AI assistance is observed.

Compensation: €60,000 to €70,000 gross annual salary (fixed) depending on experience and seniority + up to €6,000 variable bonus depending on experience and seniority

At Artefact, we are convinced that the diversity of backgrounds, experiences, and perspectives enriches our teams. We are committed to offering an inclusive environment and equitable opportunities, where everyone can grow and express their full potential. Our recruitments are based on skills, potential, and fit for the role.