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# Senior AI Platform / DevTool Engineer

- **Company:** Progress Partners
- **Location:** Inconnu, France (Remote available)
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Confluence, JIRA, Programming Tools, Github, Knowledge-Based Systems, Systems Development Life Cycle, Next.js, Software Engineering, Workflow Management Systems, Application Enhancement Tool, Large Language Models, AI Platforms, Slack, Figma
- **Published:** July 4, 2026
- **Apply:** https://fr.indeed.com/viewjob?jk=ffed7e8496d1d932

## About the Role

* 6+ years in software engineering, platform engineering, developer tooling, or related technical roles, plus 2+ years building AI-powered tools, LLM-based systems, or RAG solutions.
 * Strong software engineering background with a track record of shipping reliable, maintainable tools used by other engineers.
 * Experience designing APIs, SDKs, or reusable internal services that teams actually adopt.
 * Solid grasp of RAG architectures, embeddings, vector databases, and retrieval workflows.
 * Strong written communication and the ability to work autonomously in a remote, async environment.
 * Comfort with ownership, transparency, and a results-driven mindset., * Experience with Figma-to-code, component generation, or design-system automation.
 * Familiarity with coding assistants, AI agents, or agentic workflow orchestration (LangChain, LlamaIndex, Vercel AI SDK, or similar).
 * Experience integrating AI tooling into GitHub, Jira, Slack, Confluence, Linear, or CI/CD pipelines.
 * Background supporting external engineering teams or vendor delivery models.

## Description

We're looking for a Senior AI Platform / DevTool Engineer to build the reusable AI tooling, automation, and shared services that help our internal and external teams ship faster and more consistently. This is a platform role, not a customer-facing prompt role, you'll build the RAG systems, AI-assisted workflows, and developer tools that turn scattered individual AI usage into a real engineering capability. You'll have the freedom to go from idea to working internal tool, with real ownership over adoption, reliability, and cost., * Build internal AI tools and workflow automation that remove repeated manual work across Engineering, Product, QA, and Design.

 * Implement RAG-based knowledge systems that make product specs, technical docs, and architecture decisions actually searchable and useful.
 * Create reusable AI-assisted SDLC workflows, from product briefs and acceptance criteria to test scenarios and release documentation.
 * Evaluate and integrate coding assistants, design-to-code tooling, and other AI-enabled developer tools where they create real leverage.
 * Define secure, observable, and cost-aware patterns for AI tooling, and own usage, reliability, and cost monitoring across what you build.


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