CDI - Platform Engineer Manager - Paris

Mission

Leading groups run their AI on what you build. Cloud infrastructure, data platforms, delivery chains, model serving, agent runtime: these foundations support our clients' applications and agents at scale, and you design and operate them. Through this work, product teams deliver without risk, while costs, latency, and security remain under control.

This role is for you if your users are other engineering teams and if the robustness of a system under load interests you.

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

Activities and Responsibilities

Product Engineers and AI Engineers deploy on the platforms you build; their needs are defined with them, and their support continues into production.

On a data platform, the foundations are yours: infrastructure, security, network, compute. What circulates within it is the responsibility of Context & Analytics Engineers. Work is also conducted with the client's IT, security, and cloud teams, under whose scrutiny every architectural choice must be defensible; code is reviewed by peers, and new hires are supported.

Your responsibilities:

• Deploy cloud infrastructure as code and lead cloud migrations.

• Build and operate data platforms: warehouses and lakehouses, orchestration, batch and streaming processing.

• Build CI/CD pipelines and MLOps and LLMOps tooling.

• Build agent harnesses: runtime, evaluation frameworks, observability.

• Serve models and master their cost and latency.

• Lay the security foundations: IAM, network, secrets, agent access rights, protection against prompt injection.

• Build connectors and MCP servers that multiple agents reuse.

• Ensure reliability: monitoring, incident management, cost tracking.

Profile sought

The essentials:

1. You have built and operated cloud infrastructure on Google Cloud, Azure, or AWS, using infrastructure as code.

2. CI/CD pipelines and containerized deployments, for example on Kubernetes, are part of your practice.

3. Security basics (IAM, network rules, secret management) are applied in your daily work.

4. What you operate, you monitor, and you participate in incident management.

5. The code and documentation you write remain maintainable for the teams using your platform.

6. Coding agents are already part of your daily life, and you verify what they produce.

A plus, not a prerequisite:

• Experience with data platforms, for example on Databricks, BigQuery, or Snowflake.

• MLOps or LLMOps experience: model serving, model registry, evaluation pipelines.

• Agent observability or LLM gateway experience: tracing, cost tracking, routing.

• FinOps: cost tracking and optimization.

• AI Security: agent access rights, red teaming, guardrails.

• A cloud certification.

No need to tick all the boxes to apply: if you have the essentials and the desire to learn the rest, let's talk.

Why join us?

A platform is never finished: each new agent deployed at a client raises new questions of cost, latency, and access rights. The Platform Engineer works at the point where these questions arise first, closest to the client's technical and security teams.

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 the business. The positions we take are clear; they evolve in response to facts, never under pressure.

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

When something is missing, for a client or for the chapter, we build it. Progress 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 during your first months. Technical onboarding opens the path, followed by a first client project; if none is ready, internal starter kits take over.

The rest is learned on projects: model serving, agent observability, AI security, with the support of the Platform Engineer community. From 2027, the Platform Engineer archetype will have its own skills matrix and training path, in addition to the common foundation for all AI Applied Engineers (AI evaluation, production responsibility).

A specialization in AI security (red teaming, guardrails, AI Act compliance) is possible: it currently falls under the Platform Engineer archetype. Later, a transverse role opens up (Project Tech Lead, Pre-sales Engineer, Engineering Manager), and senior Platform Engineers can join the AI Architect chapter.

Recruitment process

The process requires approximately four hours, divided into six stages, with only one on-site.

1. HR Interview (45 min, remote): background, motivations, and practical points, such as timeline 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, then 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 building a new feature with a coding agent and presenting 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 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 allowed, or none. During live coding, the way of working with AI assistance is observed.

Compensation: €75,000 to €85,000 gross annual (fixed) + up to €8,000 variable depending on experience and seniority

At Artefact, we are convinced that 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 fully express their potential. Our recruitments are based on skills, potential, and fit for the role.