Manager: ai architecture i
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 (Gen AI), 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 (Gen AI) 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)