Lead Data & AI Solutions Architect - Government & Infrastructure

About the role

EY GDS Poland, part of the global integrated service delivery center network, is looking for a Lead Data & AI Solutions Architect for the Government & Infrastructure sector, based in Katowice. The role works 2 days in the office and 3 days remotely, within a global team on international projects.

Key responsibilities

• Serve as lead architect for the Government & Infrastructure sector, focusing on pre-sales solutioning and delivery of cloud data and AI solutions.
• Work with sales and account teams to understand client needs and tailor solutions.
• Lead the Cloud Data Architecture and Delivery teams, ensuring they have the skills to deliver high-quality, sector-specific solutions.
• Take part in client meetings and workshops, gathering insights, refining offers and presenting business cases showing ROI.
• Develop and present Go-To-Market strategies aligned with best practices in the Government and Infrastructure sector, especially around AI and data analytics.
• Take on additional technical, organizational or coordination roles as needed, and use professional networks to share best practices.

Required profile

• Proven experience in the Government, Public and Infrastructure sectors.
• 8+ years of hands-on experience building complex AI and data solutions.
• Experience in an Architect role, including designing and building complex data solutions.
• Ability to facilitate design discussions and communicate architectural decisions clearly.
• Experience creating reliable solution plans with business and multidisciplinary teams, including cost estimation and delivery approaches.
• Strategic thinking, demonstrated by researching client inquiries, regulations, industry practices and new application technologies.
• Proficiency in English (C1); French or German is an advantage.

Required skills

• Azure or AWS cloud products and services.
• Familiarity with data fabric, data mesh and data vault concepts.
• Agile methodologies (Scrum, Kanban).
• Ideally, machine learning, deep learning and exposure to large language models (LLMs).