Lead AI Solution Architect

About the role

The Lead AI Solution Architect aligns enterprise AI solutions with architectural, security and governance requirements, enabling scalable delivery from prototype through operational support. The role coordinates end-to-end AI solution integration across data sources, enterprise platforms, legacy systems and business applications, and bridges business stakeholders, Product Owners, AI engineers, developers, data and platform teams, IT and network domains.

Key responsibilities

• Align value pool and project-level AI solutions with enterprise architecture principles, security requirements, governance standards and approved platform capabilities.
• Design and coordinate end-to-end integration across data sources, enterprise platforms, legacy systems and business applications.
• Apply production-grade architecture practices and ensure solutions are scalable, reliable, observable, maintainable and ready for Day 2 support.
• Translate business requirements into clear, actionable solution designs.
• Evaluate emerging AI technologies and architectural approaches for relevant use cases.
• Balance innovation, delivery speed, solution complexity, platform fit and long-term operational sustainability.

Required profile

• Bachelor's or Master's degree in Computer Science, Engineering, Data, AI/ML or a related field.
• 5 to 10 years of experience in solution architecture or technical leadership roles, with a strong focus on AI platform integration and API-driven systems.
• Proven expertise in designing, reviewing and approving end-to-end AI workflows across hybrid cloud environments.
• Experience leading and managing development teams focused on software engineering and AI solution delivery.
• Experience evaluating and integrating new AI technologies, frameworks and vendor solutions into enterprise environments.
• Ability to communicate complex AI concepts to executive, technical and non-technical audiences and to manage stakeholders across business, IT and network domains.
• Familiarity with the telecommunications domain, IT systems and data-driven use cases; on-site working pattern.

Required skills

• AI/ML systems, cloud-native architectures and API ecosystems.
• Cloud-based AI services, including model deployment, monitoring and scaling.
• AI/ML orchestration tools and frameworks, including retrieval-augmented generation and model lifecycle management.
• REST, GraphQL and gRPC; containerisation, virtual machines, microservices and MLOps tools.
• Technical writing and presentation skills.