AI & Automation Solution Architect

Jobtailor; Des Moines (IA); On-site; USD 120,000 - 170,000; Full time

Job summary

Jobtailor in Des Moines, IA is seeking an experienced Solution Architect to lead AI and automation pattern design across business units. You will develop reusable patterns, prototypes, and reference architectures to scale adoption while aligning with enterprise standards.

You will partner with enterprise architecture, security, governance, and delivery teams to ensure production readiness, risk controls, and observable, maintainable solutions. Strong collaboration and communication are essential.

Qualifications

• 5+ years of experience across architecture, integration, or automation.
• Experience designing AI/automation solutions using APIs and integrations.
• Familiarity with enterprise cloud platforms and security considerations.
• Knowledge of governance, risk, and compliance in regulated environments.
• Ability to develop reusable patterns, templates, and reference architectures.

Responsibilities

• Design AI and automation solution patterns for business use cases.
• Build reference implementations and proofs of value.
• Provide technical advisory to business, product, and delivery teams.
• Embed responsible AI practices and governance alignment.
• Define technical standards, templates, and reusable assets.
• Support end-to-end lifecycle from intake to scale.
• Coach distributed delivery teams on patterns and tools.
• Collaborate with enterprise architecture, data, security, and governance teams.

Skills

Solution architecture

Automation engineering

Systems integration

Cloud platforms

Security-by-design

Stakeholder management

Job description

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 management, infrastructure, and operations.
  • Ensure solutions consider data readiness, integration needs, access controls, platform constraints, system performance, monitoring, operational support, scalability, and vendor/platform limitations.
  • Support alignment with approved platforms, enterprise technical standards, capability roadmaps, and technical enablement needs.

Qualifications

• 5+ years of experience in solution architecture, application development, automation engineering, systems integration, enterprise applications, digital transformation, or a related technology field required.
• Experience designing and delivering AI, automation, workflow, low-code/no-code, data-enabled, or digital business solutions using enterprise applications, APIs, integrations, and data services required.
• Experience with Microsoft cloud, AI, automation, and low-code/no-code platforms; responsible AI, AI governance, data privacy, information security, model risk, vendor risk, regulatory, or compliance considerations; and regulated industry environments such as insurance, financial services, or healthcare strongly preferred.
• Familiarity with advanced AI and automation practices, including MLOps, LLMOps, prompt engineering, retrieval-augmented generation, document intelligence, process mining, workflow orchestration, knowledge management, or API-based integrations preferred.
• Experience developing prototypes, proofs of value, reusable technical assets, reference architectures, enterprise solution patterns, or supporting design reviews, architecture governance, agile delivery, product management, design thinking, Lean, Six Sigma, DevOps, continuous improvement, communities of practice, or technical training preferred.
• Strong knowledge of solution design, systems integration, security-by-design, testing, documentation, production readiness, supportability, operational handoff, and translating business needs into practical solution options.
• Strong collaboration, problem-solving, facilitation, consulting, documentation, stakeholder management, and communication skills, with experience working across business, product, architecture, security, data, compliance, risk, and technology teams.
• Knowledge of solution architecture, systems integration, AI and automation technologies, responsible AI practices, governance, security, production readiness, and operational support considerations.
• Translate business needs into practical AI, automation, workflow, and digital solution designs aligned with enterprise standards.
• Develop reusable solution patterns, prototypes, technical templates, reference architectures, and other enablement assets that support repeatable delivery.
• Evaluate tools, platforms, integration patterns, and automation capabilities through hands‑on experimentation and documented lessons learned.
• Advise, coach, and enable business and technology teams on approved AI and automation practices, responsible use, and pilot-to-production execution.
• Partner across architecture, data, security, compliance, risk, governance, operations, and delivery teams to ensure solutions are secure, scalable, supportable, and aligned with enterprise expectations.