Solutions Architect - Databricks

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.

• 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

• 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

• 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

• 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

• 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

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…