Skills, Competencies and Experience

• The successful candidate will be required to have the following skills and competencies:
  
• A Bachelor’s degree in Computer Science, Software Engineering, Information Systems, Data Science, AI/ML, Engineering, or a related field.
  
• Professional certification in Solution Architecture, Enterprise Architecture, Cloud Architecture, API Architecture, Cybersecurity, or AI Architecture will be an added advantage.
  
• Exposure or training in AI, machine learning, analytics, cloud platforms, DevSecOps, or responsible AI will be an added advantage.
  
• A minimum of 5 years’ relevant experience in Solution Architecture, Enterprise Integration, Software Engineering, Digital Delivery, or Intelligent Systems Delivery.
  
• A strong understanding of solution architecture, enterprise integration, APIs, microservices, middleware, event-driven architecture, and enterprise platforms.
  
• Experience designing secure, scalable, resilient, and supportable solutions within complex or regulated environments.
  
• A good understanding of AI and analytics solution patterns including orchestration, automation services, retrieval-augmented generation, and intelligent decisioning.
  
• A strong understanding of non-functional requirements including performance, availability, resilience, observability, maintainability, and security.
  
• Strong stakeholder management, documentation, presentation, and communication skills with the ability to simplify complex architecture concepts for diverse audiences.
  

• Define and govern scalable, secure, and resilient AI-enabled solution architectures, integrations, and enterprise technology patterns aligned to the Bank’s digital transformation agenda.
  
• Design end-to-end architecture solutions covering applications, APIs, automation, AI orchestration, security, operational readiness, and enterprise platform integration.
  
• Translate business requirements into practical, reusable, and implementation-ready architecture solutions that deliver measurable business value.
  
• Ensure architecture decisions comply with governance, security, privacy, resilience, audit, and regulatory requirements while embedding appropriate AI risk controls and safeguards.
  
• Collaborate with cross-functional teams and delivery partners to support AI solution implementation, architecture reviews, technical quality assurance, and enterprise technology best practices.