AI Engineer / Agent Builder

About the 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

Required background

• 3 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 exposure

• Strong hands-on experience with Python

• Strong experience building with LLMs and modern AI application patterns

• Proven expertise in RAG, retrieval pipelines, embeddings, knowledge integration, and prompt engineering

• Solid understanding of fine-tuning, model behavior, evaluation, and the practical use of different LLM providers

• Experience designing agentic systems and multi-step AI workflows

• Strong understanding of guardrails, validation, reliability, and production constraints

• Experience integrating APIs, tools, data sources, and business systems into AI workflows

• Ability to move from prototype to production

• Strong business understanding and ability to work directly with non-technical stakeholders

• Fluent English required; French is a strong plus

Nice to have

• Experience with tools such as LangChain, orchestration frameworks, agent tooling, and AI workflow platforms

• Experience with Dust, n8n, or other automation / no-code orchestration tools

• Experience with internal AI use cases in Finance, Operations, Customer Success, or Business teams

• Experience in fast-paced product companies, scale-ups, or tech-driven environments

• Experience with evaluation frameworks, monitoring, and LLMOps practices

Environment / stack

Main topics and technologies involved in the mission include:

• Python

• LLMs / GenAI APIs

• RAG

• Fine-tuning

• Agentic AI / multi-agent orchestration

• LangChain and related frameworks

• Guardrails / validation layers

• APIs / webhooks / connectors

• Dust

• n8n

• Documentation, workflow automation, and internal tooling integration