The Manager: AI Architecture is responsible for designing, governing, and evolving the organization’s end-to-end Artificial Intelligence (AI) architecture. The role ensures AI solutions are scalable, secure, ethical, and aligned with business strategy while integrating seamlessly with enterprise data platforms, cloud infrastructure, applications, and technology ecosystems.

The position provides architectural leadership across AI initiatives, defining enterprise standards, reference architectures, governance frameworks, and technology roadmaps that enable the successful adoption of Machine Learning (ML), Generative AI (GenAI), intelligent automation, and advanced analytics capabilities.

Working closely with business leaders, product teams, enterprise architects, data scientists, engineering teams, security, and technology stakeholders, the role ensures AI solutions are designed for production, operational resilience, regulatory compliance, and long-term business value.

Key Performance Areas (KPAs)

1. Enterprise AI Architecture

• Define, maintain, and govern the enterprise AI architecture and technology roadmap.
• Develop architecture standards for:
  • Machine Learning (ML)
  • Generative AI (GenAI)
  • Intelligent Automation
  • AI Platforms
• Translate business strategies into scalable AI solution architectures and technical blueprints.
• Evaluate and recommend AI technologies, platforms, frameworks, and vendors aligned with enterprise objectives.

2. AI Solution Design

• Design end-to-end AI solutions covering:
  • Data ingestion
  • Data preparation
  • Feature engineering
  • Model development
  • Model deployment
  • Monitoring and optimization
• Ensure AI solutions are:
  • Production-ready
  • Scalable
  • Highly available
  • Secure
  • Observable
  • Resilient
• Define reusable architecture patterns and technical standards for enterprise AI implementations.

3. AI Governance & Responsible AI

• Establish governance frameworks covering:
  • AI model lifecycle management
  • Model governance
  • Explainability
  • Fairness
  • Transparency
  • Auditability
• Ensure AI solutions comply with:
  • Information security standards
  • Privacy requirements
  • Regulatory obligations
  • Ethical AI principles
• Define AI model approval, validation, and risk management processes.

4. AI Platform & Integration Architecture

• Design AI platforms that integrate with:
  • Enterprise data platforms
  • APIs
  • Cloud infrastructure
  • Enterprise applications
• Support implementation of:
  • MLOps
  • LLMOps
  • Continuous Integration/Continuous Deployment (CI/CD)
  • Automated monitoring
  • Drift detection
  • Model retraining
• Align AI architecture with enterprise data, cloud, security, and application architectures.

5. Technical Leadership & Collaboration

• Partner with:
  • Business leaders
  • Product teams
  • Data Scientists
  • AI Engineers
  • Enterprise Architects
  • Technology teams
• Provide technical leadership and mentorship across AI and data engineering teams.
• Serve as the organization’s AI architecture subject matter expert for strategic technology initiatives.
• Promote architecture best practices, innovation, and continuous improvement.

Governance

Strategic & Operational Governance

• Lead and participate in AI architecture governance forums and technology review boards.
• Contribute to enterprise AI strategy and capability roadmaps.
• Drive enterprise-wide AI transformation initiatives in collaboration with business and technology stakeholders.
• Establish governance processes for:
  • AI architecture standards
  • Technology change management
  • Service level policies
  • AI operational procedures
• Review and approve architectural changes impacting the AI ecosystem.

Escalation Management

• Resolve architecture and technology issues affecting AI platforms, solutions, and operational processes.
• Provide technical guidance on architectural risks, design decisions, and technology trade-offs.

Reporting

• Report periodically to executive leadership on:
  • AI architecture initiatives
  • Technology roadmap progress
  • Architecture compliance
  • Delivery milestones
  • Key performance indicators
• Prepare executive reports and technical updates for strategic initiatives as required.

Job Requirements

Education

• Bachelor’s Degree in:
  • Computer Science
  • Information Systems
  • Artificial Intelligence
  • Data Science
  • Software Engineering
  • Information Technology
  • Or a related technical discipline
• Relevant professional certifications in Cloud, AI, Enterprise Architecture, or Solution Architecture are advantageous.

Experience

• Minimum 5 years’ experience in AI Architecture, Solution Architecture, Enterprise Architecture, or Data Architecture roles.
• Demonstrated experience designing and delivering enterprise-scale AI solutions.
• Experience within large-scale or highly regulated organizations.
• Experience with:
  • Cloud-native AI platforms
  • Big Data ecosystems
  • Enterprise integration architecture
  • API-driven platforms
• Strong background in:
  • Solution Architecture
  • Systems Integration
  • Technical Design
  • Enterprise Architecture
• Proven experience designing AI solutions that incorporate Responsible AI principles, including:
  • Fairness
  • Explainability
  • Transparency
  • Privacy
  • Security
  • Model governance
  • Quality assurance
• Experience working with structured, semi-structured, and unstructured data throughout the AI lifecycle.
• Experience collaborating across diverse business functions and geographically distributed teams is advantageous.

Technical Competencies

AI Solution Architecture

• Enterprise AI architecture
• Machine Learning architecture
• Generative AI architecture
• Intelligent automation platforms
• AI solution design

Large Language Models (LLMs)

• Large Language Models
• AI Agents
• Retrieval-Augmented Generation (RAG)
• Prompt engineering
• Agent orchestration

Responsible AI

• Fairness
• Explainability
• Bias detection and mitigation
• Transparency
• Privacy
• Security
• AI governance
• Regulatory compliance

AI Lifecycle Management

• Model evaluation
• Testing and validation
• Monitoring
• Drift detection
• Performance optimization

MLOps & LLMOps

• CI/CD pipelines
• Model deployment
• Automated retraining
• Observability
• Lifecycle management

Data & Platform Architecture

• Structured, semi-structured, and unstructured data
• Feature engineering
• Data readiness
• Cloud-native AI platforms
• Enterprise integration architecture

Risk Management

• AI model risk
• Data risk
• Operational risk
• Technology governance

Skills

• Business acumen
• Strategic thinking
• Enterprise architecture
• Analytical and critical thinking
• Decision making
• Digital transformation leadership
• Project and program management
• Executive communication and presentation
• Stakeholder engagement
• Conflict resolution
• Negotiation
• Financial and commercial awareness
• People leadership and mentoring
• Managing ambiguity and complexity

Behavioural Competencies

• Strategic and innovative thinker
• Adaptable and resilient
• High integrity and professional ethics
• Collaborative and inclusive leadership style
• Strong emotional intelligence
• Results-oriented
• Culturally aware
• Accountable and decisive
• Trusted technical advisor
• Continuous learning mindset

Authorities

• Operate within the organization’s Delegation of Authority (DOA) framework.

Collaboration

Internal Stakeholders

• Executive Leadership
• Enterprise Architecture
• Product Management
• AI Engineering Teams
• Data Science Teams
• Data Engineering
• Cloud Engineering
• Information Security
• Risk & Compliance
• Technology Operations
• Business Units

External Stakeholders

• Cloud Service Providers
• AI Technology Vendors
• Enterprise Technology Partners
• Systems Integrators
• Consulting Partners
• Industry and Standards Bodies
• External Auditors (where applicable)