Ai Engineer - Freelance

Présentation de La MISSION

We 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 mission The 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 quickly * an industrialization phase, focused on strengthening architecture, reliability, guardrails, quality, and scalability Main responsibilities 1. Design and build AI agents * Design, develop, and deploy internal AI agents for operational and business teams * Build agentic workflows able to handle multi-step reasoning and action execution * Develop robust systems using LLMs, RAG pipelines, prompt engineering, memory, tool use, and guardrails * Work on more advanced AI topics when relevant, including fine-tuning, evaluation, and model adaptation * Contribute to multi-agent orchestration patterns when needed 2. Turn business processes into AI products * Work closely with business teams to understand workflows, bottlenecks, and automation opportunities * Translate operational needs into technical AI solutions * Identify the right level of solution maturity: quick MVP, advanced prototype, or production-grade system * Prioritize use cases with tangible business value and measurable impact 3. Industrialize and productionize solutions * Move from MVPs to robust production systems * Improve reliability, observability, maintainability, and scalability of AI agents * Implement proper validation layers, fallback mechanisms, monitoring, and governance * Ensure solutions are usable in real business environments, not just demos 4. Integrate with the internal ecosystem * Connect AI agents to internal tools, APIs, data sources, and workflow systems * Build integrations with business applications, reporting layers, databases, knowledge bases, and documentation systems * Leverage automation tools when relevant, while keeping a strong technical ownership of the overall architecture * Ensure agents can perform concrete actions across internal systems in a secure and controlled way 5. Contribute to the AI engineering foundation * Help structure best practices for internal AI development * Contribute to standards around architecture, prompting, RAG design, evaluation, and guardrails * Support the creation of a scalable AI engineering approach across the company * Act as a key technical contributor in the build-up of a future AI team