Senior Enterprise Architect, AI Solutions

WGU Advancement

Enterprise Architect focusing on AI architecture strategy and implementation at WGU, transforming online education with innovative AI solutions. Collaborating across teams to enhance student success and operational efficiency.

Posted 5/22/2026full-timeSalt Lake City • North Carolina, Utah • 🇺🇸 United StatesSenior💰 $175,900 - $272,600 per yearWebsite

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGoogle Cloud PlatformKafkaMicroservices

About the role

Key responsibilities & impact

Define and lead WGU’s enterprise AI architecture strategy, including reference architectures, standards, and best practices.
Establish patterns for integrating AI, ML, and generative AI solutions into enterprise systems in a secure, scalable, and maintainable way.
Architect AI-enabled solutions such as recommendation systems, intelligent assistants, and automation workflows that align to institutional priorities.
Define reusable AI services and platforms, including model serving, prompt orchestration, inference pipelines, and shared enablement capabilities.
Strengthen the data and integration foundations that support AI, including pipelines, data quality, feature engineering readiness, APIs, microservices, and event-driven patterns.
Enable real-time and adaptive AI use cases by designing event contracts, streaming architectures, and feedback loops that are interoperable across domains.
Establish responsible AI and governance frameworks that support ethical, secure, and compliant AI usage, including privacy considerations and FERPA alignment.

Requirements

What you’ll need

10 plus years of experience in software engineering, architecture, or related roles, with increasing focus on AI and ML systems.
3 years of experience as an Enterprise Architect or Solution Architect, or 8 years in a technical leadership role such as technical lead or principal engineer.
Proven experience designing enterprise-scale distributed systems and delivering successful technology transformation.
Proven experience designing and deploying AI, ML, or generative AI solutions at scale.
Strong expertise in AI and ML architecture, including training, inference, and deployment patterns.
Strong expertise in APIs and microservices patterns used to integrate AI capabilities into enterprise applications.
Strong expertise with distributed systems and cloud platforms such as AWS, Azure, or GCP, including architectural tradeoffs and scalability considerations.
Bonus Points
Experience with LLMs, prompt engineering, RAG architectures, and vector databases.
Familiarity with MLOps tools and frameworks such as MLflow, SageMaker, or Vertex AI.
Familiarity with modern data stack tools such as Snowflake, Databricks, dbt, or Kafka, and how they support AI workloads.

Benefits

Comp & perks

eligible for bonuses
medical, dental, vision, telehealth and mental healthcare
health savings account and flexible spending account
basic and voluntary life insurance
disability coverage
accident, critical illness and hospital indemnity supplemental coverages
legal and identity theft coverage
retirement savings plan
wellbeing program
discounted WGU tuition
flexible paid time off for rest and relaxation with no need for accrual
flexible paid sick time with no need for accrual
11 paid holidays
other paid leaves, including up to 12 weeks of parental leave

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

AI architectureML architecturegenerative AI solutionsenterprise-scale distributed systemsAPIsmicroservicescloud platformsLLMsprompt engineeringMLOps

Soft Skills

leadershipcommunicationstrategic planningproblem-solvingcollaborationethical considerationsgovernance frameworksadaptabilityinnovationtechnical mentorship