The posting
The key focus for the senior data/AI architect is to perform planning aligned to key AI solutions, build and participate in the architecture capability building, perform AI architecture and design, manage AI architecture risk and compliance, provide design and build governance and support and communicate and share knowledge around the architecture practices, guardrails, blueprints and standards related to the AI solution design. A key focus of this role is partnering with the AI Technology Centre of Excellence (AI Tech COE) to build out the organisation's Databricks AI platform and support the delivery of enterprise AI and generative AI use cases.
Planning
- Lead AI solution requirements gathering and ensure alignment with business objectives and constraints.
- Define and refine AI architecture runways for intentional architecture with the key stakeholders
- Provide input into business cases and costing
- Participate and provide AI architectural runway requirements into Programme Increment (PI) Planning
Architecture Capability
- Design and implement enterprise-grade AI architectures leveraging Databricks and cloud-native technologies.
- Develop and oversee AI architecture views and ensure alignment with enterprise architecture.
- Maintain and oversee the AI solution artifacts in the set enterprise repository and knowledge portals aligned to the rest of the architecture
- Manage the AI architecture processes based on the requirements for each architype
- Manage change impact of the AI architecture with stakeholders
- Develop and participate in the build of the AI architecture practice with embedded architects and engineers including the relevant methods, repository and tools
- Manage the AI architecture considering the business, application, information/data and technology viewpoints
- Establish, enforce and implement AI standards, guardrails, frameworks, and patterns
- Partner with the AI Tech COE to define and evolve the Databricks AI platform architecture, ensuring alignment with enterprise data and AI strategy
- Design and implement AI/ML architectures on Databricks, including MLOps pipelines, model lifecycle management, Unity AI Gateway and Unity Catalog governance for AI/ML assets Planning Lead AI solution requirements gathering and ensure alignment with business objectives and constraints. Define and refine AI architecture runways for intentional architecture with the key stakeholders Provide input into business cases and costing Participate and provide AI architectural runway requirements into Programme Increment (PI) Planning Architecture Capability Design and implement enterprise-grade AI architectures leveraging Databricks and cloud-native technologies. Develop and oversee AI architecture views and ensure alignment with enterprise architecture. Maintain and oversee the AI solution artifacts in the set enterprise repository and knowledge portals aligned to the rest of the architecture Manage the AI architecture processes based on the requirements for each architype Manage change impact of the AI architecture with stakeholders Develop and participate in the build of the AI architecture practice with embedded architects and engineers including the relevant methods, repository and tools Manage the AI architecture considering the business, application, information/data and technology viewpoints Establish, enforce and implement AI standards, guardrails, frameworks, and patterns Partner with the AI Tech COE to define and evolve the Databricks AI platform architecture, ensuring alignment with enterprise data and AI strategy Design and implement AI/ML architectures on Databricks, including MLOps pipelines, model lifecycle management, Unity AI Gateway and Unity Catalog governance for AI/ML assets Solution Design Lead and review logical and detailed AI architecture Evaluate and approve AI solution options and technology selections Select appropriate technology, tools and build for the solution Oversee and maintain the AI solution blueprints Drive incremental modernisation initiatives in the delivery area Design and evaluate architectures for AI and generative AI use cases, including RAG pipelines, vector stores, feature stores, and LLM integration patterns Risk, Governance and Compliance Identify, assess and mitigate risks at a AI solution architecture level Ensure and enforce compliance with policies, standards, and regulations Lead AI architecture reviews and integrate with governance functions Integrate with other governance and compliance functions to ensure continuity in managing the investment and risk for the organisation pertaining to the solution architectures Establish and provide AI standards, guidance, and tools to delivery teams. Implementation and Collaboration Establish and provide AI solution architectures and tools to the delivery and AI engineering teams Lead and facilitate collaboration with delivery teams to achieve architecture objectives Manage and resolve deviations and ensure up-to-date AI solution design documentation Identify opportunities to optimise delivery of solutions Oversee and conduct post-implementation reviews Ensure the AI architecture supports CI/CD pipelines to facilitate rapid and reliable deployment of data solutions Implement automated testing frameworks for AI solutions to ensure quality and reliability throughout the development lifecycle. Establish performance monitoring and optimisation practices to ensure AI solutions meet performance benchmarks and can scale as needed. Integrate robust AI security measures, including encryption, access controls, and regular security audits, into the implementation process. Communication and Knowledge Sharing Communicate and advocate up-to-date AI solution architecture views Communicate the relevant AI standards, practices, guardrails and tools to stakeholders relevant to the solution design Ensure IT teams are well-informed and trained in architecture requirements Communicate and collaborate with stakeholders' relevant views on planning, technology assessments, risk, compliance, governance and implementation assessments Foster collaboration between AI architects, AI engineers, and other IT teams through regular cross-functional meetings and agile ceremonies. Communicate and maintain up-to-date blueprint designs for key data solutions Ensure effective participation in the agile ceremonies (PI planning, sprint planning, retrospectives, demos) Implement regular feedback loops with stakeholders and end-users to continuously improve data solutions based on real-world usage and requirements Create a culture of knowledge sharing by organising regular workshops, training sessions, and documentation updates to keep all team members informed about the latest AI architecture practices and tools Requirements and experience Matric Degree or diploma in Information Technology, Computer Science, Engineering OR relevant diploma / degree Data and AI Experience: Required a minimum of 7 years related experience in AI, data engineering, data modeling and design and data management and governance Expert-level proficiency in Databricks, including Delta Lake, Spark, and MLflow. Proven experience architecting and delivering AI/ML solutions on Databricks, including MLOps, model deployment and monitoring, and Unity Catalog governance for AI/ML assets. Hands-on experience in large-scale data and AI platform implementation (preferably cloud-based)
