The salary range for this position takes into account the wide range of factors that are considered in making compensation decisions including but not limited to skill sets; experience and training; licensure and certifications; and other business and organizational needs.
At WGU, it is not typical for an individual to be hired at or near the top of the range for their position, and compensation decisions are dependent on the facts and circumstances of each case. A reasonable estimate of the current range is:
Grade: Technical 412
Pay Range: $161,000.00 - $249,500.00
Job Description
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 6 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.
#LI-AW2
Position & Application Details
Full-Time Regular Positions (classified as regular and working 40 standard weekly hours): This is a full-time, regular position (classified for 40 standard weekly hours) that is 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; and 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, and other paid leaves, including up to 12 weeks of parental leave.
How to Apply: If interested, an application will need to be submitted online. Internal WGU employees will need to apply through the internal job board in Workday.
Additional Information
Disclaimer: The job posting highlights the most critical responsibilities and requirements of the job. It 27s not all-inclusive.
