Sr. Enterprise Architect, AI Solutions

Western Governors University•Salt Lake City, UT

1d•Hybrid

About The Position

If you’re passionate about building a better future for individuals, communities, and our country—and you’re committed to working hard to play your part in building that future—consider WGU as the next step in your career. Driven by a mission to expand access to higher education through online, competency-based degree programs, WGU is also committed to being a great place to work for a diverse workforce of student-focused professionals. The university has pioneered a new way to learn in the 21st century, one that has received praise from academic, industry, government, and media leaders. Whatever your role, working for WGU gives you a part to play in helping students graduate, creating a better tomorrow for themselves and their families. In this role, you will help accelerate innovation and transformation by shaping how AI is responsibly designed and embedded across the enterprise to improve student success, personalization, and operational efficiency.

Requirements

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.

Nice To Haves

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.
WGU considers a combination of relevant education, certifications, and directly related experience when evaluating qualifications for this role.

Responsibilities

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.