We are seeking an experienced Senior Solution / Enterprise Architect to lead the design and delivery of enterprise‐scale technology solutions, including AI-enabled and data‐driven platforms. This role will be responsible for defining end‐to‐end architecture, ensuring alignment with business objectives, and establishing robust standards for security, governance, integration, and scalability.
Key Responsibilities
Solution Architecture & Strategy
* Lead the end‐to‐end architecture design for enterprise platforms, digital solutions, and AI‐enabled systems aligned with business strategy and technology roadmaps.
* Translate business requirements into scalable, secure, and maintainable architectural solutions.
* Define architecture patterns for AI integration, intelligent automation, and advanced analytics within enterprise systems.
* Provide architectural leadership across multiple initiatives and ensure alignment with enterprise architecture principles.
* Evaluate and recommend technology platforms, frameworks, and AI/ML tools that support innovation and long‐term scalability.
* Design and integrate AI‐powered capabilities such as predictive analytics, machine learning models, natural language processing, and intelligent decision systems.
* Define architecture patterns for AI model integration within enterprise applications and workflows.
* Ensure responsible AI implementation including model governance, explainability, security, and ethical use of AI technologies.
* Collaborate with data science and engineering teams to operationalise machine learning models (MLOps) into production environments.
* Design scalable data pipelines and AI infrastructure to support training, deployment, and monitoring of AI models.
* Produce and maintain comprehensive architecture artefacts, including solution diagrams, integration models, API designs, AI workflows, data flows, and deployment architectures.
* Document architectural decisions, trade‐offs, assumptions, and risks to support transparency and maintainability.
* Establish architecture standards, frameworks, and best practices across engineering and data teams.
* Define integration patterns and API strategies to enable secure, scalable, and reliable communication between systems.
* Ensure seamless interoperability between internal platforms, AI services, data platforms, and third‐party systems.
* Guide the design of platform services that support microservices, cloud‐native architectures, and AI service integration.
Data, Security & Governance
* Design robust data architecture frameworks supporting analytics, reporting, AI models, and operational performance.
* Ensure strong data security and privacy controls, including encryption, access management, and data lifecycle governance.
* Establish architecture patterns that promote data integrity, privacy protection, and responsible AI usage.
Reliability, Performance & Resilience
* Design systems with high availability, resilience, and fail‐safe mechanisms to ensure operational continuity.
* Ensure platforms support high‐performance data processing and AI workloads.
* Incorporate monitoring, logging, observability, and AI model performance monitoring to support operational excellence.
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