Manager

The Opportunity: Your Next Adventure Awaits? 

AI is moving from experimentation to enterprise execution. Organisations now need leaders who can connect ambition to architecture, data foundations to deployed products, and emerging technology to measurable business value. At EY Belgium, our Data & AI practice is growing rapidly, and we are investing significantly in the capabilities, talent and propositions that will define our next phase.

We work with leading organisations on Artificial Intelligence, Generative AI, Agentic AI, data platforms, AI-ready data foundations, AI engineering, data engineering, intelligent operations and responsible adoption. Our work spans strategy, engineering and transformation: from shaping an AI Strategy or Data Strategy to building scalable solutions, modernising Data Architecture and embedding new ways of working.

This is an opportunity to build, not just to manage. As Senior Manager – AI & Data, you will join us at a moment of momentum and help shape what comes next. You will operate at the intersection of business strategy × AI/Data × engineering × leadership—moving comfortably from boardroom discussions and executive workshops to architecture decisions, engineering teams, AI prototypes, transformation roadmaps and commercial conversations.

Your Key Responsibilities

You will lead complex Data & AI engagements from first conversation through implementation and value realisation. You will work directly with C-level and senior stakeholders, structure ambiguous problems, shape transformation roadmaps and turn business priorities into secure, scalable technology solutions.

• Lead with outcomes. Own major AI Transformation, Digital Transformation and Data & AI programmes, keeping business value, adoption, governance and delivery quality in view.
• Shape the answer. Define AI Strategy, Data Strategy, target operating models, transformation roadmaps and investment priorities with senior client leaders.
• Design for scale. Guide AI Architecture, Data Architecture, cloud-native architectures and modern Data Platforms, including integration, APIs, streaming and event-driven patterns.
• Stay close to the build. Oversee—and where relevant contribute to—solution design, prototypes and engineering decisions across Generative AI, LLMs, RAG, AI Agents, Machine Learning, MLOps and LLMOps.
• Lead multidisciplinary teams. Bring together AI Engineering, Data Engineering, architecture, product, industry, change and governance specialists; coach them and create the conditions for exceptional work.