Engineer H/F - collectivite

Information importanteType de contrat: FreelanceTaux journalier : Salaire selon profilLocalisation : Paris, FranceDate de démarrage :UrgentMode de travail : HybridePublié le : 29 juin 2026Le besoinAI Engineer / AI Agent Builder (Freelance)Location: Paris or BordeauxStart: ASAPContract: Freelance, full-timeOn-site: 4 days per weekAbout the missionWe are supporting a fast-growing French unicorn looking to accelerate the industrialization of AI across the organization.The objective is not simply to test AI use cases, but to build a real internal AI capability able to design, develop, deploy, and scale AI agents that automate high-value business workflows across multiple teams such as Finance, Accounting, Customer Success, Business, and other operational functions.This is a highly technical, end-to-end role for someone who combines strong expertise in LLMs, agentic systems, RAG, fine-tuning, orchestration, and Python engineering with the ability to understand business processes and turn them into production-ready AI systems.The consultant will first help identify and build high-impact MVPs for internal teams, then progressively industrialize those solutions into more robust and scalable agent-based systems.Scope of the missionThe consultant will be responsible for designing and building internal AI agents and agentic workflows that can automate and augment business processes at scale.This includes both:a rapid MVP phase, focused on understanding business needs and delivering useful first versions quicklyan industrialization phase, focused on strengthening architecture, reliability, guardrails, quality, and scalabilityMain responsibilities1. Design and build AI agentsDesign, develop, and deploy internal AI agents for operational and business teamsBuild agentic workflows able to handle multi-step reasoning and action executionDevelop robust systems using LLMs, RAG pipelines, prompt engineering, memory, tool use, and guardrailsWork on more advanced AI topics when relevant, including fine-tuning, evaluation, and model adaptationContribute to multi-agent orchestration patterns when needed2. Turn business processes into AI productsWork closely with business teams to understand workflows, bottlenecks, and automation opportunitiesTranslate operational needs into technical AI solutionsIdentify the right level of solution maturity: quick MVP, advanced prototype, or production-grade systemPrioritize use cases with tangible business value and measurable impact3. Industrialize and productionize solutionsMove from MVPs to robust production systemsImprove reliability, observability, maintainability, and scalability of AI agentsImplement proper validation layers, fallback mechanisms, monitoring, and governanceEnsure solutions are usable in real business environments, not just demos4. Integrate with the internal ecosystemConnect AI agents to internal tools, APIs, data sources, and workflow systemsBuild integrations with business applications, reporting layers, databases, knowledge bases, and documentation systemsLeverage automation tools when relevant, while keeping a strong technical ownership of the overall architectureEnsure agents can perform concrete actions across internal systems in a secure and controlled way5. Contribute to the AI engineering foundationHelp structure best practices for internal AI developmentContribute to standards around architecture, prompting, RAG design, evaluation, and guardrailsSupport the creation of a scalable AI engineering approach across the companyAct as a key technical contributor in the build-up of a future AI teamRequired background3 to 6+ years of experience in a highly technical role such as AI Engineer, Machine Learning Engineer, Applied AI Engineer, LLM Engineer, or Software Engineer with strong AI exposureStrong hands-on experience with PythonStrong experience building with LLMs and modern AI application patternsProven expertise in RAG, retrieval pipelines, embeddings, knowledge integration, and prompt engineeringSolid understanding of fine-tuning, model behavior, evaluation, and the practical use of different LLM providersExperience designing agentic systems and multi-step AI workflowsStrong understanding of guardrails, validation, reliability, and production constraintsExperience integrating APIs, tools, data sources, and business systems into AI workflowsAbility to move from prototype to productionStrong business understanding and ability to work directly with non-technical stakeholdersFluent English required; French is a strong plusNice to haveExperience with tools such as LangChain, orchestration frameworks, agent tooling, and AI workflow platformsExperience with Dust, n8n, or other automation / no-code orchestration toolsExperience with internal AI use cases in Finance, Operations, Customer Success, or Business teamsExperience in fast-paced product companies, scale-ups, or tech-driven environmentsExperience with evaluation frameworks, monitoring, and LLMOps practicesEnvironment / stackMain topics and technologies involved in the mission include:PythonLLMs / GenAI APIsRAGFine-tuningAgentic AI / multi-agent orchestrationLangChain and related frameworksGuardrails / validation layersAPIs / webhooks / connectorsDustn8nDocumentation, workflow automation, and internal tooling integrationWhat the client is looking forThis is not a pure low-code automation role.The priority is a consultant with a strong technical depth in modern AI engineering, able to:understand business processesbuild AI MVPs quicklydesign more advanced architectures afterwardindustrialize internal AI agents in a scalable and production-oriented wayThe ideal profile is someone who can operate end-to-end: from identifying the right use case with business teams to designing and implementing the technical AI solution behind it.

• Python
• Anglais
• Reporting
• API
• Intelligence artificielle
• Automation
• Création d'une base documentaire
• Français