AI Solution Architect
Confirmed on the employer's own hiring board on Oct 11, 2026. First seen by Alion on Oct 6, 2026.
JOB PURPOSE:
To design and architect end-to-end AI solutions that are scalable, secure, and aligned with CIB’s enterprise architecture standards. The role is responsible for translating complex business and data requirements into robust AI solution blueprints, ensuring technical coherence across the full AI development lifecycle from experimentation through production deployment.
The AI Solution Architect serves as the technical authority for AI solution design within the AI Engineering function, establishing patterns, standards, and reusable frameworks that accelerate delivery quality and consistency across AI initiatives.
KEY ACCOUNTABILITIES:
Roles and Responsibilities
1. Design and document end-to-end AI solution architectures, including data flows, model integration patterns, APIs, and deployment topologies, ensuring alignment with enterprise architecture principles and security standards.
2. Translate business requirements and AI use cases into detailed technical specifications, solution blueprints, and architectural decision records that guide development and engineering teams.
3. Define and maintain AI architecture standards, design patterns, and reusable component libraries to accelerate delivery and ensure consistency across AI initiatives.
4. Evaluate and recommend AI frameworks, platforms, infrastructure components, and third-party tools, conducting proof-of-concept assessments to validate architectural choices before broader adoption.
5. Collaborate with Data and AI Engineering teams to ensure that solution designs are implementable, scalable, and operationally sustainable in production environments.
6. Work with Enterprise Data & AI Governance and Risk functions to embed explainability, fairness, auditability, and regulatory compliance requirements into solution designs from the outset.
7. Coordinate with IT infrastructure, security, and enterprise architecture teams to ensure AI solutions are designed within approved infrastructure boundaries and meet applicable security control requirements.
8. Assess and manage technical risks associated with AI solution designs, including scalability constraints, model performance degradation, integration complexity, and vendor dependencies.
9. Support the AI Engineering Manager and Head of AI Engineering in scoping, estimating, and planning AI initiatives, providing architectural input into project proposals and roadmap prioritization.
10. Maintain awareness of emerging AI architectures, LLM integration patterns, RAG frameworks, and agentic AI design paradigms, and evaluate their applicability within the bank’s on-premises and hybrid infrastructure context.
11. Conduct architectural reviews of AI solutions developed by engineering teams, ensuring adherence to approved standards, patterns, and governance requirements prior to production deployment.
12. Contribute to the formulation and execution of the AI Engineering strategy, ensuring architectural decisions are aligned with CIB’s overall technology roadmap and strategic objectives.
13. Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB’s sound legal position and mitigate any potential risks
14. Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB’s sound legal position and mitigate any potential risks.
Policies, Processes and Procedures
15. Follow all relevant department policies, processes, standard operating procedures and instructions so that work is carried out in a controlled and consistent manner
Day-to-day Operations
16. Follow the day-to-day operations related to own jobs in the department to ensure continuity of work
JOB PURPOSE:
To design and architect end-to-end AI solutions that are scalable, secure, and aligned with CIB’s enterprise architecture standards. The role is responsible for translating complex business and data requirements into robust AI solution blueprints, ensuring technical coherence across the full AI development lifecycle from experimentation through production deployment.
The AI Solution Architect serves as the technical authority for AI solution design within the AI Engineering function, establishing patterns, standards, and reusable frameworks that accelerate delivery quality and consistency across AI initiatives.
KEY ACCOUNTABILITIES:
Roles and Responsibilities
1. Design and document end-to-end AI solution architectures, including data flows, model integration patterns, APIs, and deployment topologies, ensuring alignment with enterprise architecture principles and security standards.
2. Translate business requirements and AI use cases into detailed technical specifications, solution blueprints, and architectural decision records that guide development and engineering teams.
3. Define and maintain AI architecture standards, design patterns, and reusable component libraries to accelerate delivery and ensure consistency across AI initiatives.
4. Evaluate and recommend AI frameworks, platforms, infrastructure components, and third-party tools, conducting proof-of-concept assessments to validate architectural choices before broader adoption.
5. Collaborate with Data and AI Engineering teams to ensure that solution designs are implementable, scalable, and operationally sustainable in production environments.
6. Work with Enterprise Data & AI Governance and Risk functions to embed explainability, fairness, auditability, and regulatory compliance requirements into solution designs from the outset.
7. Coordinate with IT infrastructure, security, and enterprise architecture teams to ensure AI solutions are designed within approved infrastructure boundaries and meet applicable security control requirements.
8. Assess and manage technical risks associated with AI solution designs, including scalability constraints, model performance degradation, integration complexity, and vendor dependencies.
9. Support the AI Engineering Manager and Head of AI Engineering in scoping, estimating, and planning AI initiatives, providing architectural input into project proposals and roadmap prioritization.
10. Maintain awareness of emerging AI architectures, LLM integration patterns, RAG frameworks, and agentic AI design paradigms, and evaluate their applicability within the bank’s on-premises and hybrid infrastructure context.
11. Conduct architectural reviews of AI solutions developed by engineering teams, ensuring adherence to approved standards, patterns, and governance requirements prior to production deployment.
12. Contribute to the formulation and execution of the AI Engineering strategy, ensuring architectural decisions are aligned with CIB’s overall technology roadmap and strategic objectives.
13. Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB’s sound legal position and mitigate any potential risks
14. Comply with all relevant CBE regulations, banking laws, AML regulations and internal CIB policies and code of conduct in order to maintain CIB’s sound legal position and mitigate any potential risks.
Policies, Processes and Procedures
15. Follow all relevant department policies, processes, standard operating procedures and instructions so that work is carried out in a controlled and consistent manner
Day-to-day Operations
16. Follow the day-to-day operations related to own jobs in the department to ensure continuity of work
Qualifications & Experience:
Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, or a related field. A Master’s degree in AI, Machine Learning, or a relevant discipline is preferred.
8+ years of progressive experience in solution architecture, AI/ML engineering, or advanced analytics, with demonstrated expertise in designing and deploying enterprise-grade AI solutions into production environments.
Minimum 3 years of dedicated AI solution architecture experience, including model deployment architectures, API integration patterns, and production-grade AI infrastructure design.
Proven experience designing LLM-based solutions, including RAG pipelines, prompt engineering frameworks, agentic architectures, and integration patterns for large language model deployment.
Experience in financial services or similarly regulated industries, with understanding of architecture governance, risk management frameworks, and regulatory compliance requirements for AI systems.
Solid grasp of distributed systems, containerization, API design, and on-premises or hybrid infrastructure principles relevant to AI solution deployment.
Skills:
Exceptional ability to synthesize complex technical and business requirements into clear, implementable AI solution architectures with well-documented rationale and trade-off analysis.
Strong knowledge of AI/ML modelling approaches, deep learning frameworks, and model serving technologies, with the ability to assess technical trade-offs objectively.
Solid understanding of data architecture principles, including data pipelines, feature stores, vector databases, and integration with enterprise data platforms.
Effective communicator capable of presenting architectural designs and technical decisions to non-technical stakeholders, governance forums, and executive management.
Collaborative working style with the ability to build consensus across Data, IT, Risk, and business teams on architectural choices.
Awareness of responsible AI principles, including fairness, explainability, privacy-by-design, and bias mitigation, and the ability to embed these into solution design practices.
Structured problem-solving approach with the ability to navigate ambiguity, manage technical complexity, and deliver pragmatic architectural decisions under delivery constraints.
Qualifications & Experience:
Bachelor’s degree in Computer Science, Computer Engineering, Software Engineering, Artificial Intelligence, or a related field. A Master’s degree in AI, Machine Learning, or a relevant discipline is preferred.
8+ years of progressive experience in solution architecture, AI/ML engineering, or advanced analytics, with demonstrated expertise in designing and deploying enterprise-grade AI solutions into production environments.
Minimum 3 years of dedicated AI solution architecture experience, including model deployment architectures, API integration patterns, and production-grade AI infrastructure design.
Proven experience designing LLM-based solutions, including RAG pipelines, prompt engineering frameworks, agentic architectures, and integration patterns for large language model deployment.
Experience in financial services or similarly regulated industries, with understanding of architecture governance, risk management frameworks, and regulatory compliance requirements for AI systems.
Solid grasp of distributed systems, containerization, API design, and on-premises or hybrid infrastructure principles relevant to AI solution deployment.
Skills:
Exceptional ability to synthesize complex technical and business requirements into clear, implementable AI solution architectures with well-documented rationale and trade-off analysis.
Strong knowledge of AI/ML modelling approaches, deep learning frameworks, and model serving technologies, with the ability to assess technical trade-offs objectively.
Solid understanding of data architecture principles, including data pipelines, feature stores, vector databases, and integration with enterprise data platforms.
Effective communicator capable of presenting architectural designs and technical decisions to non-technical stakeholders, governance forums, and executive management.
Collaborative working style with the ability to build consensus across Data, IT, Risk, and business teams on architectural choices.
Awareness of responsible AI principles, including fairness, explainability, privacy-by-design, and bias mitigation, and the ability to embed these into solution design practices.
Structured problem-solving approach with the ability to navigate ambiguity, manage technical complexity, and deliver pragmatic architectural decisions under delivery constraints.