Ai Architect

• Ensure the technical consistency of AI solutions and their portability across target environments, acting as the custodian of architectural standards and best practices
• In collaboration with data, IT, and business stakeholders, establish robust data and technology foundations that guarantee scalability, reliability, security, and trust in AI solutions
• Advise on solution design and target architecture for AI applications, guiding teams towards sound, future-proof technical choices
• Contribute to drafting technical specifications, follow up on development progress, and oversee acceptance testing to ensure solutions meet quality and performance standards
• Oversee the seamless integration, management, and scaling of AI systems, coordinating between data teams, IT, and business functions
• Monitor AI infrastructure performance, manage costs, and ensure operational reliability, including scheduling model updates and managing dependencies
• Participate in, and potentially lead, dedicated project governance and technical forums, ensuring architectural decisions are well-documented and understood
• Proactively identify technical risks and propose mitigation strategies to protect delivery timelines and solution integrity.

• 8 years of professional experience with at least 5 years of experience in DevOps, cloud infrastructure, IT architecture, or a closely related field, with meaningful hands-on exposure to AI tooling and platforms
• Solid understanding of AI and ML components, including model serving, data pipelines, vector databases, and orchestration frameworks
• Proven experience setting architectural standards or reference architectures across multiple teams or projects
• Experience reviewing, challenging, and approving technical decisions made by other engineers or vendors
• Experience with major cloud providers (Azure, AWS, or GCP), with a strong preference for candidates familiar with Azure and Databricks
• Proficiency in infrastructure-as-code, CI/CD pipelines, containerisation (Docker, Kubernetes), and monitoring tooling
• Good understanding of data architecture principles, including data warehousing, data lakes, and governance frameworks
• Ability to translate technical constraints and risks into clear language for business and project stakeholders
• Strong attention to detail and a systematic approach to quality assurance and testing
• Fluency in English.

Experience in international, consulting, or scale-up environments is a plus.

• The opportunity to design and build the technical backbone that powers a large-scale enterprise AI transformation
• A role at the intersection of cloud infrastructure, data engineering, and applied AI, with real architectural ownership
• Regular collaboration with senior technical and business stakeholders across a complex, international organization
• Exposure to a wide variety of AI tools, platforms, and frameworks in a fast-evolving technology landscape
• A dynamic, diverse team environment with strong peer learning and knowledge-sharing culture.