AI Tooling Engineer

Job description

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for an AI Tooling Engineer based in France. We are looking for an AI Tooling Engineer to develop and scale an internal agentic platform that brings together company knowledge, data, and tools in one secure, accessible environment. Working closely with a Principal Engineer, you will build production-ready AI agents and integrations that help employees and their AI assistants access the information they need. You will take ownership of the full development lifecycle, from understanding user needs and designing solutions to deployment, monitoring, and continuous improvement. Using technologies such as Python, LangGraph, LLM APIs, and Model Context Protocol (MCP), you will transform prototypes into reliable tools used across the organization. You will also establish evaluation frameworks, optimize AI performance and costs, and ensure robust security and access controls. This remote opportunity offers the chance to shape company-wide AI capabilities within a globally distributed, technology-driven team. Accountabilities: You will help develop and maintain an internal AI platform, combining agent development, systems integration, production engineering, and continuous optimization to deliver practical tools that improve how teams work. Collaborate with the Principal Engineer to design, develop, and deploy new platform capabilities, taking ownership from initial design through production release. Integrate internal systems and SaaS applications by connecting APIs and data sources, defining shared data models, and implementing appropriate access permissions. Build AI agents and supporting tools, including MCP servers that enable agents across the organization to interact with internal systems and retrieve relevant context. Design and improve retrieval workflows, tool calling, embeddings, prompts, and context management to deliver accurate and useful AI-generated responses. Establish and maintain AI quality standards through evaluation datasets, benchmarks, regression testing, tracing, and observability. Monitor performance indicators, including cost per answer and token usage, and optimize models, prompts, and workflows for efficiency and reliability. Troubleshoot production issues, incorporate user feedback, and continuously improve the platf…