Episode 37 — Organizational Roles: Who Does What on an AI Team
This episode explores the organizational roles necessary for building and sustaining AI systems. Teams often include data scientists, data engineers, machine learning engineers, product managers, ethicists, and business stakeholders. Understanding how these roles collaborate is essential for certification exams, which may test recognition of responsibilities and dependencies. Clear division of labor ensures that models are not only technically sound but also aligned with organizational goals and ethical standards.
We illustrate this with applied scenarios. Data engineers prepare and manage pipelines, while data scientists design and train models. Machine learning engineers focus on deployment and optimization, while product managers ensure outputs meet business needs. An ethicist or governance officer may review systems for fairness and compliance. Troubleshooting considerations include overlapping responsibilities or unclear accountability, which can slow projects or introduce risks. Best practices stress cross-functional communication, documentation, and iterative alignment across teams. Exam questions may describe team structures and ask which role is missing or responsible for a given task. By mastering organizational roles, learners understand the human foundation behind technical success. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your certification path.
