AI & Automation Solution Architect

Responsibilities

• Design AI and Automation Solution Patterns
  • Develop reusable AI and automation solution patterns that translate business use cases into practical conceptual, logical, and technical designs.
  • Define repeatable design approaches, documentation standards, testing expectations, support considerations, and measurement practices that can be adopted across business and technology teams.
  • Partner with enterprise architecture to ensure solution patterns align with enterprise standards, integration principles, security expectations, and cloud architecture direction.
• Build Reference Implementations and Proofs of Value
  • Build or co-build prototypes, proof-of-value implementations, reusable components, and demonstration scenarios that validate AI and automation approaches.
  • Evaluate emerging tools, platforms, integration patterns, automation capabilities, and technical assumptions through hands‑on reference implementations.
  • Document implementation lessons, constraints, and recommended patterns so solutions can be replicated, adapted, extended, and moved toward production.
• Provide Technical Advisory and Design Support
  • Serve as a technical advisor to business, product, process improvement, and technology delivery teams pursuing AI and automation opportunities.
  • Translate use cases, workflow impacts, business requirements, and adoption needs into practical solution options in partnership with the AI & Automation Enablement Lead.
  • Guide teams through tool selection, architecture, integration, data, security, controls, testing, deployment, scaling considerations, and technical risk tradeoffs.
• Support Responsible AI and Automation Practices
  • Embed responsible AI and automation practices into solution design, including transparency, human oversight, data protection, security, privacy, explainability where appropriate, and appropriate use limitations.
  • Partner with governance, risk, compliance, legal, privacy, information security, and data governance teams to ensure solutions follow enterprise guardrails.
  • Identify, document, and support mitigation of AI‑specific risks, production readiness needs, human‑in‑the‑loop controls, operational monitoring, and auditability requirements.
• Define Technical Standards, Templates, and Reusable Assets
  • Create and maintain technical templates, reference architectures, design checklists, prompt engineering patterns, automation standards, testing guides, and production readiness materials.
  • Develop reusable components, scripts, connectors, workflow patterns, prompt libraries, configuration examples, and technical accelerators where appropriate.
  • Partner with CoE leadership to ensure technical assets are understandable, reusable, aligned with business‑facing playbooks, and continuously improved based on implementation lessons.
• Support Lifecycle Execution from Intake to Scale
  • Support the end‑to‑end AI and automation lifecycle, from opportunity assessment and solution design through prototype development, governance alignment, testing, deployment readiness, adoption, measurement, and scale.
  • Define production readiness expectations for technical design, documentation, ownership, monitoring, support model, controls, adoption needs, and benefit tracking.
  • Partner with delivery teams to plan pilot‑to‑production transitions, resolve technical barriers, and ensure solutions are secure, supportable, observable, maintainable, and aligned with enterprise architecture expectations.
• Enable Distributed Delivery Teams
  • Coach technology and business teams on approved AI and automation patterns, tools, standards, delivery practices, and responsible use of CoE‑provided guidance and reusable assets.
  • Provide technical enablement through demos, design walkthroughs, knowledge‑sharing sessions, office hours, and communities of practice.
  • Help teams determine when to use generative AI, workflow automation, RPA, low‑code/no‑code tools, APIs, data services, or traditional application capabilities while promoting reuse over one‑off solutions.
• Partner Across Platforms, Data, Architecture, and Security
  • Collaborate with enterprise architecture, cloud/platform teams, data and analytics, application teams, security, identity/access…