Adjunct Instructor - Applied AI Lab

Under the direct supervision of the Applied AI Lab Executive Director, the AI instructor is responsible for delivering high-quality, hands-on instruction in artificial intelligence (AI) and potential subfields of AI such as generative AI, and machine learning. The instructor will engage learners in an active, applied learning environment aligned with the mission, goals, and strategic direction of the Applied AI Lab.

This role emphasizes up-to-date, real-world AI practices and is aimed at preparing learners (primarily at beginner and intermediate levels) with skills that are relevant, and industry driven. Training assignments may include instruction for both public and private contract training. Most instruction will be conducted in person, with the possibility of hybrid or online delivery in the future. Some travel may be required.

1. Deliver engaging, hands-on instruction in AI-related topics such as:
   • Machine learning fundamentals, including supervised/unsupervised learning, evaluation metrics, and model deployment basics
   • Natural language processing (NLP) fundamentals and applications (e.g., text classification, summarization, embeddings)
   • Generative AI techniques and tools, including prompt engineering, fine-tuning, and use of large language models (LLMs)
   • End-to-end GenAI workflows, including data preparation, model selection, API integration (e.g., OpenAI, Cohere, Anthropic), and output evaluation
   • Agentic systems and multi-step reasoning, using frameworks like LangChain, AutoGen, or similar to build goal-directed AI agents
   • Use of vector databases and retrieval-augmented generation (RAG) for building scalable, intelligent applications
   • Responsible AI practices, including bias detection, model transparency, and safe deployment of generative systems

2. Teach using modern tools and platforms such as Python, PyTorch, Hugging Face, LangChain, TensorFlow, OpenAI API, and other open-source or commercial GenAI tools.

3. Apply active learning strategies to accommodate a variety of learning styles and professional backgrounds.
4. Collaborate with the Applied AI Lab Specialist to review and refine existing curriculum for relevance and effectiveness, with oversight from Applied AI Lab Executive Director.
5. Proactively suggest new AI-related curriculum ideas and topic areas to reflect current and emerging trends in the AI ecosystem.
6. Maintain professional and technical knowledge through continuous learning and practical experience in the AI field.
7. Provide training during or outside of normal business hours, on or off the Pewaukee campus, depending on learner or client needs.
8. Use learner feedback and performance data to improve instructional effectiveness.
9. Maintain positive, professional relationships with learners, clients, and the Lab team.

REQUIRED KNOWLEDGE, SKILLS, AND ABILITIES:  

• Strong understanding of AI/ML concepts and the ability to explain them clearly to learners with varying levels of experience
• Practical experience with at least two of the following: Python, PyTorch, Hugging Face, LangChain, TensorFlow, OpenAI API, Cohere, Model Context Protocol (MCP)or similar tools/protocols/platforms.
• Excellent communication, organization, and instructional skills, with the ability to simplify complex technical topics
• Proficiency in using applied learning methods, including project-based instruction and real-world use cases
• Ability to work independently, as part of a team, and with minimal supervision
• Familiarity with Zoom or other virtual learning platforms preferred

EDUCATIONAL AND EXPERIENCE REQUIREMENTS:

• Associate degree or equivalent in computer science, data science, AI, or a related field required; Bachelor's or Master’s degree preferred for certain subject areas
• At least 24 months of directly related occupational experience in AI, ML, NLP, or data science; one year of experience must be within the last five years
• Prior teaching, mentoring, or training experience is highly desirable, particularly with adult learners or working professionals
• Relevant certifications (e.g., TensorFlow Developer, AWS ML Specialty, OpenAI Training, etc.) are a plus