AI Engineer (f/m/d)

Publiée le 13/08/2026

Mission

About Decathlon

Decathlon aspires to become the world's best sports digital platform and open ecosystem. We want to enable customers to experience Decathlon through numerous local sports-centric experiences by connecting many third-party actors and services in a secure and high-performance manner.

Our digital teams in Lille and Paris (and more...) grouping over 5000 employees, are united to design and develop digital products with the goal of always offering the best value to our users. Present in over 70 countries, Decathlon is committed to innovation, sustainability, and customer satisfaction.

Job Description

Our PRISM (Supply Chain) is looking for an AI Engineer based in Paris. As an AI Engineer dedicated to agent deployment, you will be at the heart of a tight-knit technical team. Your will play a critical role in the industrialization, deployment and monitoring of our Agent capabilities. You will closely work with digital teams (Data, Product, Engineering) to architect the deployment infrastructure, design intelligence routing, and build secure infrastructure allowing our agents to interact natively with our corporate data and legacy systems.

Responsibilities

• Design and deploy solid foundations that will allow our AI agents to be hosted, run, and interact smoothly with our systems during production release.
• Design robust pipelines enabling multi-step asynchronous agentic execution (e.g., transactional ingestion and writing flows), by implementing distributed resilience patterns.
• Integrate agentic orchestration SDKs (notably Google Vertex AI Agent SDK / ADK), bypassing "black box" limitations of managed frameworks.
• Efficiently orchestrate agents in production while ensuring proper information processing, routing to the most suitable models, and guaranteeing optimal cost and token usage management. Also ensure quality and speed of task execution.
• Strictly define the scope of action for each agent. You guarantee they only access data and actions for which they are formally authorized, respecting our confidentiality rules, while ensuring data security.
• Build advanced performance measurement and tracking pipelines to trace agent reflections and decisions from end to end in order to debug logic in production.
• Work with the team to define relevant and consistent data evaluation metrics relative to the defined functional specificities and metrics.

Hard Skills

• Robust backend design (Python), operational mastery of containerization (Docker, Kubernetes) and pipelines (CI/CD, automated testing, observability, log management) to ensure robust production releases.
• Production-focused Python expertise, mastery of deployment platforms (Vertex AI Agent Builder, Gemini Enterprise Agent Platform) and client/server integration protocols like the Model Context Protocol (MCP).
• Experience optimizing context windows for massive frontier models. Request routing to models suitable for the selected solutions.
• Ability to build test frameworks for LLMs (LLM-as-a-Judge, Golden Datasets), mastery of deterministic metrics and strict validation of output formats (JSON schema enforcement).
• Practical experience with Agentic RAG, Semantic Caching to reduce latency and API costs, or using prompt optimization frameworks (e.g., DSPy)
• Applied experience in supply chain, retail technology, or model deployment via Databricks / MLFlow

Soft Skills

• Excellent communication and collaboration skills.
• Ability to work effectively in a dynamic and fast-paced environment.
• Strong problem-solving skills and attention to detail.
• Adaptability and willingness to learn new technologies and methodologies and share them.
• Ability to translate complex technical concepts into understandable terms for non-technical stakeholders.
• Ability to understand user challenges and associated needs.

Qualifications

• Senior Profile: You have solid experience (Machine Learning Engineering) with a strong focus on production infrastructure release.
• Ability to leverage managed solutions (Vertex AI) while knowing how to "dive into code" to debug distributed architectures, develop custom connectors, and bypass framework limitations.
• Demonstrated ability to evolve in shifting technical environments.
• Operational skills on GCP and understanding of containerized environments (Docker, Kubernetes) to autonomously deploy the Agentic layer.

Technical Environment

• Agent orchestration: Gemini Enterprise Agent Platform, Vertex AI Agent Builder
• Infrastructure & Cloud: GCP (Google