Staff AI Engineer

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. 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 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