The role is responsible for designing, governing, and evolving the end-to-end artificial intelligence architecture of the organisation. The position ensures AI solutions are scalable, secure, ethical, and aligned with business strategy, while integrating seamlessly with existing data, cloud, and enterprise architectures. The incumbent acts as the AI subject-matter expert, providing technical leadership across AI and data functions and driving enterprise-wide AI transformation initiatives.

Responsibilities

Architecture & Strategy

Define and own the enterprise AI architecture, including machine learning, generative AI, and intelligent automation platforms
Translate business and product strategies into AI solution blueprints and reference architectures
Evaluate and select AI technologies, frameworks, and vendors aligned to strategic goals
Design end-to-end AI solutions, from data ingestion and feature engineering to model deployment and monitoring
Ensure AI solutions are production-grade, scalable, observable, and resilient
Architect AI platforms integrated with data platforms, APIs, cloud services, and enterprise systems
Align AI architecture with enterprise data, cloud, and security architectures

Governance & Risk

Establish AI governance frameworks, including model lifecycle management, explainability, fairness, and auditability
Ensure compliance with regulatory, security, privacy, and ethical AI standards
Define model risk management and approval processes
Sign off on approvals for changes to the data architecture environment
Prepare proposals on change initiatives, service level agreement policies, and procedures for AI solutions and strategy
Drive adequate risk mitigation and controls, engaging relevant stakeholders for input

Delivery & Operations

Support MLOps and LLMOps capabilities, including continuous integration/continuous deployment, monitoring, drift detection, and retraining
Guide engineering teams on AI solution design, deployment patterns, and best practices
Manage and resolve escalations impacting AI systems and processes
Lead and participate in planned and ad hoc meetings on AI strategy and capability roadmaps

Leadership & Stakeholder Engagement

Partner with business leaders, product teams, data scientists, engineers, and architects
Provide technical leadership and mentorship across AI and data teams
Report periodically to leadership on progress against defined metrics, and on an ad hoc basis for specific projects

Experience and education

Education

Bachelor's degree in Computer Science, Information Systems, Data Management, or a related technical field
Relevant professional certification, accreditation, or body membership as required

Experience

5+ years of experience in AI, data, or solution architecture roles
Proven experience delivering enterprise-grade AI solutions in large-scale or regulated environments
Experience with cloud data platforms and big data ecosystems is advantageous
Experience in the financial services, banking, or telecommunications sector is preferred; exposure to mobile money is an added advantage
Experience in integration and orchestration, functional architecture design, and technical solution design
Demonstrated ability to architect end-to-end AI solutions with embedded responsible AI principles, including fairness, explainability, transparency, privacy, security, model governance, and quality assurance/validation methodologies
Proven capability working with structured and unstructured data across the full AI lifecycle
Experience working across diverse cultures and geographies

Functional Knowledge

Strong AI solution architecture capability, with the ability to design end-to-end, enterprise-scale AI solutions integrating data, models, platforms, and business processes
Strong understanding of large language models, AI agents, retrieval-augmented generation, prompt design, and orchestration patterns
In-depth knowledge of responsible AI and ethics, including bias mitigation, transparency, privacy, security, and regulatory compliance
Expertise in model evaluation, testing, monitoring, drift detection, and performance validation in production environments
Practical MLOps/LLMOps experience, including deployment, retraining, and lifecycle management
Ability to assess and manage model, data, and operational risk associated with AI solutions
Cloud and platform expertise