**AI Developer/Engineer (Agentic/Gen AI) - Paris (on-site) -- EU Public Institution**

**Profile:**Developer.

**Minimum experience:**3 to 5 years.

**Knowledge:**AI.

**Studies required**: Technical Engineer.

**Language:** English (C1) **MANDATORY.**

**Location:**Paris, on-site.

**Contract Type:** Freelance / Contract. Flexible. The rate offered depends on the candidate's level, in accordance with the European public grading system. Further details are available upon discussion.

**DESCRIPTION:**

The AI Developer/Engineer will be responsible for designing, developing, and implementing agentic AI solutions focused on creating robust AI workflows and integrating Large Language Model (LLM)-based agents into existing business systems and processes. The role involves building scalable and production-ready AI capabilities, orchestrating intelligent workflows, and collaborating with cross-functional teams to ensure secure and effective deployment of AI solutions. The position is full-time and on-site in Paris.

**Description of the tasks:**

· Design and develop agentic AI workflows aligned with business and technical requirements.

· Orchestrate AI agents, tools, prompts, and decision-making flows.

· Integrate LLM capabilities with existing applications, APIs, and data sources.

· Implement workflow state management mechanisms to ensure reliable execution.

· Develop error-handling processes and human-in-the-loop interventions where required.

· Test, optimize, and industrialize AI workflows for production environments.

· Collaborate with architecture, development, and DevOps teams to ensure secure, scalable, and maintainable AI deployments.

**Knowledge and skills:**

· Experience designing and implementing agentic AI solutions.

· Knowledge of LangChain and LangGraph for AI workflow development.

· Understanding of LLM-based agents and their integration into enterprise systems and processes.

· Ability to orchestrate AI agents, prompts, tools, and decision flows.

· Experience integrating AI capabilities with applications, APIs, and multiple data sources.

· Knowledge of workflow state management concepts and implementation.

· Understanding of error handling and human-in-the-loop workflow mechanisms.

· Experience testing, optimizing, and preparing AI workflows for production environments.

· Knowledge of secure and scalable AI deployment practices in collaboration with architecture and DevOps teams.