Join WGU's AI Engineering Enablement team and play a pivotal role in shaping the next generation of intelligent, scalable systems. As a Senior AI Engineer, you will lead the design and delivery of complex AI solutions that directly impact innovation across the organization. You'll act as a technical anchor for the team, driving architecture, reliability, and excellence in production AI systems while mentoring others and influencing long-term technical direction.
What You'll Do
Lead end-to-end design and delivery of production LLM-powered applications and multi-agent systems from architecture through deployment and monitoring
Own and evolve critical AI infrastructure, ensuring reliability, scalability, and observability standards
Architect and optimize advanced RAG pipelines, including hybrid retrieval, re-ranking, and multi-index strategies
Design and implement AI evaluation frameworks, including automated regression testing, LLM-as-judge workflows, and red-teaming protocols
Drive prompt engineering strategy for complex multi-turn and multi-agent interactions, including governance and versioning practices
Lead model fine-tuning and adaptation initiatives using techniques such as LoRA, PEFT, and RLHF
Mentor AI engineers and elevate team standards through design reviews, code reviews, and technical guidance
What You'll Bring
5+ years of experience in software engineering, data science, or machine learning.
3+ years of hands-on experience building and deploying LLM-based applications or AI systems in production.
Strong experience building and deploying production LLM-powered applications using major APIs and/or open-source models
Deep knowledge of agentic AI systems including tool use, memory architectures, and multi-agent coordination
Advanced expertise in RAG pipeline design, embedding strategies, and retrieval optimization
Experience designing scalable evaluation frameworks, including automated testing and quality measurement
Proficiency in Python and strong software engineering fundamentals, including system design and CI/CD practices
Experience with agentic frameworks (LangChain, LlamaIndex, AutoGen, CrewAI, or equivalent) in production contexts.
Ability to lead complex technical discussions, mentor others, and operate effectively in fast-moving environments
Bonus Points
Experience with Databricks or related certifications
Experience with open-source model ecosystems (Hugging Face, Ollama, vLLM) and self-hosted inference infrastructure.
Experience with AWS and cloud-native AI infrastructure
Background in EdTech, personalized learning, or student-facing AI applications
Experience in Lieu of Education
An equivalent combination of relevant education and experience performing advanced AI engineering work will be considered.
At WGU, our mission drives everything we do, including how we hire. Our interview experience is designed to give qualified candidates the opportunity to show their best work through meaningful conversations and collaboration. We thoughtfully review every application and invite forward the candidates whose experience and potential best align with the role and our mission.
Introductory call with a Talent Partner
Hiring manager interview
Technical interview with senior team member
Team panel interview with live coding exercise
Work Location
This is a full-time, in-office position requiring five days per week in our Raleigh, NC office, designed to foster the collaboration and connection that fuel our best work.
Visa Sponsorship
While we welcome applicants from all backgrounds, WGU is not able to provide visa sponsorship for this role.
Equivalents and Substitutions
Equivalent relevant experience may substitute for degree requirements (1 year of experience per year of education at the discretion of the Hiring Manager).