Machine Learning & AI Topics
Production machine learning systems, model development, deployment, and operationalization. Covers ML architecture, model training and serving infrastructure, ML platform design, responsible AI practices, and integration of ML capabilities into products. Excludes research-focused ML innovations and academic contributions (see Research & Academic Leadership for publication and research contributions). Emphasizes applied ML engineering at scale and operational considerations for ML systems in production.
Artificial Intelligence and Learning Trends
Evaluates awareness of emerging technologies and how artificial intelligence and related trends are being applied to learning and development. Topics include personalized learning recommendations, automated feedback and assessment, content generation and summarization, chat based learner support, skills inference and mapping, advanced learning analytics, and the ethical and privacy considerations of deploying these tools. Interviewers will probe practical use cases, trade offs, governance and adoption concerns, and how new capabilities can shift a program from content delivery toward performance enablement.
AI and Machine Learning Background
A synopsis of applied artificial intelligence and machine learning experience including models, frameworks, and pipelines used, datasets and scale, production deployment experience, evaluation metrics, and measurable business outcomes. Candidates should describe specific projects, roles played, research versus production distinctions, and technical choices and trade offs.