Enterprise AI Integration Engineer

Inspired by human vision, PROPHESEE's technology combines patented sensors and AI algorithms to go beyond the limits of conventional approaches. It captures information differently, revealing dynamics that remain invisible to traditional systems. This breakthrough redefines computer vision and opens exciting technical challenges in cutting-edge fields such as autonomous vehicles, industrial automation, robotics, IoT, security and surveillance.

You will join a unique technological adventure where you will have the opportunity to deploy artificial intelligence at the heart of the company’s tools and processes. As the engineer driving the AI-Powered Company initiative, your work will have a direct impact on the success of one of the most promising Deep Tech companies worldwide by making AI practical, secure and useful across the organization.

The position is based in Paris (75011)

Mission

Implement the company’s AI-Powered Company initiative by integrating AI technologies into company workflows, tools and information systems, and by building the technical glue required to make AI capabilities usable, secure and maintainable across the organization. This is an implementation and integration role rather than a pure AI research position.

Role description

• Design and implement integrations between AI systems and enterprise applications.

• Deploy and operate practical AI architectures using LLMs, APIs, agents, RAG, embeddings, vector databases and orchestration frameworks.

• Build connectors, APIs, scripts and glue logic required to integrate systems and automate workflows.

• Connect AI capabilities to internal knowledge sources, collaboration platforms, development tools and business applications.

• Implement agentic workflows capable of retrieving information and orchestrating actions across authorized tools.

• Translate business needs into technical solutions and drive implementations from prototype to production.

• Work with IT and business stakeholders on identity, access control, confidentiality, governance and traceability.

• Favor pragmatic integration of commercial tools, internal systems and custom development over unnecessary custom rebuilds.

Key deliverables

• Documented target architecture for the AI-Powered Company initiative.

• Prioritized AI use cases translated into deployable technical solutions.

• Production-ready connectors, APIs, RAG components, agent workflows and automations.

• Secure integrations with company knowledge sources and enterprise applications.

• Operational documentation covering architecture, permissions, data flows and maintenance.

• Reusable integration patterns and components that accelerate subsequent AI use cases.

Performance indicators

• Number and business relevance of AI use cases moved from prototype to operational use.

• User adoption and measurable reduction of manual effort for implemented workflows.

• Reliability and maintainability of deployed AI integrations and automations.

• Quality and relevance of information retrieval and task execution for implemented use cases.

• Compliance with agreed identity, access, confidentiality, governance and traceability requirements.

• Lead time from validated business need to deployed solution.

Profile

• AI systems integration — strong requirement — Ability to integrate AI models, assistants, APIs, enterprise applications and internal information systems into end-to-end solutions.

• AI architecture — strong requirement — Understanding of LLMs, agents, RAG, embeddings, vector databases, context management and orchestration.

• Hands-on implementation — strong requirement — Ability to build and deploy solutions directly, not only define architectures or coordinate suppliers.

• Programming / glue logic — required — Ability to develop scripts, APIs, connectors and automation, preferably in Python.

• Enterprise tool integration — important — Experience connecting document systems, knowledge bases, collaboration platforms, development tools and business applications.

• Agentic AI / workflow automation — important — Understanding of AI agents and tool-orchestrated business workflows.

• Data / knowledge architecture — desirable — Understanding of information structuring, indexing, governance and controlled exposure to AI systems.

• Cloud / API / integration technologies — desirable — REST APIs, authentication, databases, containers, cloud platforms and integration frameworks.

• Project-oriented mindset — desirable — Ability to translate business requirements into implementation plans and coordinate stakeholders.

• Security & governance awareness — desirable — Understanding of access control, confidentiality, traceability and enterprise AI governance.