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IBM

Data and AI Architect

Riyadh, Saudi Arabia

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hirly's read of this role

Seniority
Mid level
Country
SA
Work mode
On-site / unstated
First seen by hirly
27 Sept 2026

Derived automatically from the posting. Upload your resume above to see how the role scores against it.

the posting

A career in IBM Consulting is built on long-term client relationships and close collaboration worldwide. You’ll work with leading companies across industries, helping them shape their hybrid cloud and AI journeys. With support from our strategic partners, robust IBM technology, and Red Hat, you’ll have the tools to drive meaningful change and accelerate client impact. At IBM Consulting, curiosity fuels success. You’ll be encouraged to challenge the norm, explore new ideas, and create innovative solutions that deliver real results. Our culture of growth and empathy focuses on your long-term career development while valuing your unique skills and experiences. As a Solution Architect: Cognitive Computing, you will design and implement cognitive solutions that run on multiple platforms and are composed of multiple software packages. You will create the technical solution architecture for a given business problem and lead the development, integration, and testing of the solution. Your primary responsibilities will include: • Design Cognitive Solutions: Create technical solution architectures for business problems, utilizing system engineering principles, cloud architectures, and probabilistic and stochastic systems. • Lead Solution Development: Guide the development, integration, and testing of cognitive solutions, ensuring performance architecture and engineering of a fully functional cognitive system. • Select Analytics Components: Choose complementary analytics components for developing solution blueprints, considering content formats, representations, data sources, content management systems, and interfaces. • Develop Solution Blueprints: Account for the full information path from source to knowledge base and from input to processing the content in Cognitive Computing solutions. • Engineer Cognitive Systems: Apply system decomposition techniques and system synthesis using available cognitive technology, including the Watson technology suite. • Deep Understanding of System Engineering Principles: Experience with system engineering principles, cloud architectures, probabilistic and stochastic systems, and system decomposition techniques to design and implement cognitive solutions. • Proficiency in Cognitive Technology: Deep expertise in cognitive technology, including the Watson technology suite, to select complementary analytics components and develop solution blueprints. • Content Management Systems Knowledge: Experience with content formats, representations, data sources, content management systems, and interfaces to ensure seamless integration and processing of content in Cognitive Computing solutions. • Performance Architecture Expertise: Experience in performance architecture and engineering of fully functional cognitive systems, considering scalability, reliability, and efficiency. • Solution Development Leadership: Experience leading the development, integration, and testing of complex cognitive solutions, guiding cross-functional teams to achieve desired outcomes. • Familiarity with Cloud Platforms: Deep expertise in cloud architectures, including scalability, reliability, and efficiency considerations, to design and implement cognitive solutions. • Knowledge of Probabilistic Systems: Experience with probabilistic and stochastic systems to develop solution blueprints and engineer cognitive systems. • Understanding of Data Sources: Experience with various data sources, content formats, and representations to ensure seamless integration and processing of content in Cognitive Computing solutions. Saudi Arabia Software Engineering Professional Riyadh, SA Riyadh (1036) IBM Middle East FZ-LLC Saudi Arabia Branch Analytics Architecture Artificial Intelligence Content Management Systems Design Engineering Integration Interfaces Leadership Reliability Scalability Systems Engineering Testing Efficiency Cloud Platforms Technology Cognitive Computing

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