Information about the Organization:

Eruditus and its sister company Emeritus provide Private Online Courses (SPOCs) to students all over the world. We have partnered with top schools across the globe, such as Columbia Business School, The Tuck School of Business at Dartmouth, MIT, Wharton, London Business School, Cambridge, and UC Berkeley Haas School of Business, to provide condensed, intense, practical, and convenient programs, which can either be conducted completely or partially online. EMERITUS enables working professionals who cannot enroll into full-time courses to access a top-tier, affordable education that will give them the skills needed to be the business leaders of tomorrow. The Emeritus and Eruditus global team include 1,000+ employees located in the US, Dubai, Mexico, India, China, and Singapore.

Information about the role:

Emeritus is seeking an experienced Applied AI / Agentic AI practitioner to support UC Berkeley Executive Education programs as a technical subject matter expert and learning partner. The expert will deliver live, interactive sessions focused on the practical application of modern AI technologies, with primary emphasis on:

• Generative AI and LLM applications
• Retrieval-Augmented Generation (RAG)
• Agentic AI and Multi-Agent Systems
• LLMOps and production of LLM applications
• Multimodal AI

The role is intended for a hands-on practitioner who has personally built, implemented, tested, evaluated, or deployed AI-powered applications or systems. The expert should be able to bring practical implementation experience into the classroom through technical demonstrations, project walkthroughs, real-world examples, and interactive discussions. Experience with classical Machine Learning, Deep Learning, NLP, Computer Vision, or traditional AI is a plus, but is not the primary focus of this role.

Key Responsibilities of the Industry Expert Speaker include but are not limited to:

• Deliver Live Applied AI Sessions: Deliver approximately 90-minute live sessions covering selected topics within:
  • LLM application development
  • RAG and semantic retrieval
  • Agentic AI and multi-agent systems
  • LLM evaluation and LLMOps
  • Multimodal AI Sessions should be practical and interactive, combining technical concepts with demonstrations, implementation examples, and learner Q&A.
• Demonstrate Real-World AI Systems Use examples from professional experience to demonstrate how modern AI applications are built and used. Depending on the session, this may include:
  • LLM application workflows
  • Embeddings and vector databases
  • RAG pipelines
  • Semantic search and retrieval
  • Reranking and context grounding
  • AI agents and tool calling
  • Agent planning, memory, and orchestration
  • Multi-agent workflows
  • LLM evaluation and prompt testing
  • AI application monitoring and observability
  • Guardrails and production considerations
  • Multimodal AI applications These areas directly reflect the proposed live-session topics in the program.
• Connect Theory to Practice Help learners understand:
  • How these technologies are used in real applications
  • Key architectural and implementation decisions
  • Common technical challenges and trade-offs
  • Practical considerations when moving from experimentation to working applications
  • Current developments in Applied and Agentic AI
• Facilitate Technical Discussions Answer learner questions during live sessions. Explain technical concepts clearly to learners with a foundational AI/ML background. Encourage discussion around implementation approaches and real-world use cases. Walk learners through code, architectures, demos, or projects where appropriate.
• Collaborate with the Instructional Team Review and contribute to session materials. Recommend relevant tools, frameworks, demonstrations, and case studies. Suggest practical activities or project examples. Ensure the sessions complement rather than duplicate the existing curriculum.