As a Staff AI Engineer, you will serve as a technical leader and force multiplier within the AI Engineering Enablement team. You will define how AI systems are architected, deployed, and scaled across the organization. Operating at the intersection of deep technical expertise and organizational leadership, you will shape platform strategy, influence roadmaps, and elevate engineering capabilities across multiple teams.
What You'll Do
Define and own AI engineering architecture standards, design patterns, and platform conventions for LLM-based systems
Lead complex, cross-functional AI initiatives from inception through delivery, aligning engineering, product, data science, and research stakeholders
Drive build-vs-buy and vendor evaluation decisions for AI frameworks, models, and infrastructure
Design and scale internal AI platforms including shared tooling, reusable components, prompt libraries, and evaluation infrastructure
Establish and mature LLMOps practices including governance, cost management, observability, and safe deployment standards
Lead AI safety initiatives including red-teaming, adversarial testing, and responsible AI policy development
Mentor and develop Senior and II-level engineers through coaching, design reviews, and technical leadership
What You'll Bring
Expert-level, production-proven experience across the AI engineering stack including LLM APIs, agentic systems, RAG pipelines, evaluation frameworks, and LLMOps
Demonstrated ability to define and drive architectural patterns and engineering standards at team or organizational scale
Deep expertise in agentic system design including multi-agent architectures, state management, and reliability engineering for non-deterministic systems
Strong platform engineering experience designing shared infrastructure, reusable tooling, and developer-facing systems
Advanced knowledge of LLM fine-tuning, alignment techniques, and evaluation methodologies including safety and bias assessment
Experience leading vendor evaluations and technical due diligence for AI frameworks and infrastructure
Strong proficiency in Python and software engineering fundamentals with a focus on quality, testing, and reliability standards
Bachelor's Degree in Computer Science, Software Engineering, Data Science, Machine Learning, Math, or a related field. Master's Degree strongly preferred.
7+ years of experience in software engineering, data science, or machine learning
5+ years of hands-on experience building and deploying LLM-based or AI systems in production at scale
Demonstrated experience setting architectural direction across teams or organizations
Experience leading complex AI projects across multiple teams or functional areas
Proven mentorship of Senior and/or II-level engineers
Experience designing and operating shared AI platforms or internal AI infrastructure
Experience owning LLMOps or MLOps practices including governance, rollout strategy, and production monitoring
Experience with AWS cloud architectures including scalable inference, data pipelines, and cost optimization
Hands-on experience with fine-tuning, PEFT, and model evaluation in production environments
Bonus Points
Master's Degree or PhD in Computer Science, AI/ML, or a related field
Experience with Databricks and related certifications
AWS certifications such as AWS Certified Machine Learning – Specialty
Experience with open-source model ecosystems and self-hosted inference infrastructure
Experience in EdTech, personalized learning, or student-facing AI platforms
Published research, conference presentations, or open-source contributions in AI/ML
Experience with enterprise AI governance, compliance frameworks, or regulatory requirements
Experience in Lieu of Education
Equivalent relevant experience performing the essential functions of this job may substitute for graduate education degree preferences or requirements.
What to Expect
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
Hiring manager interview
Technical interview
Final panel interview
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.
Travel Requirement
This position requires occasional travel of up to 20%, including required attendance at designated company summits (typically one to two per year). Additional travel may include conferences, visits to company locations, and other business-related events as needed. Additional travel may be assigned as needed to support business requirements.