The new AI roles. One page per role, grounded in a dated source snapshot.

Builds applications and features using AI models, tools and enterprise data.

Applies AI models to a customer or business problem.

Works within the customer's workflow to connect a business problem to a working technical solution.

Embeds in a business function to build the agents and workflows that team uses.

Builds workflows that connect agents, tools and multiple steps.

Builds agents that use tools, manage context and act within defined permissions.

Designs, tests, and maintains prompt suites and evals.

Builds the loops, tools and evaluation harnesses that agents run inside.

Trains, evaluates and deploys machine-learning models.

Builds, adapts and operates applications and services around large language models.

Designs the architecture and integrations for an AI system, including model services, data, permissions, observability and failure handling.

Develops and tests ideas for model architectures, training methods and evaluation.

Builds the data layer AI runs on: ingestion, feature stores, embeddings, retrieval.

Connects an AI implementation to the customer’s operating systems.

Builds and maintains model-serving, agent-runtime and evaluation infrastructure used by delivery teams.

Reviews AI systems for prompt injection, model supply-chain risks, agent permissions and data boundaries.

Leads the operation of machine-learning systems, including deployment pipelines, monitoring, retraining and rollback.

Helps customers evaluate and implement an AI product.

Builds or configures AI workflows for campaign planning, segmentation, content operations and creative production.

Builds or configures AI assistance for financial planning, accounting, audit and treasury work.

Builds or configures AI workflows for recruitment, learning, performance and internal mobility.

Builds or configures AI assistance for contract review, compliance monitoring and regulatory writing.

Builds or configures AI workflows for lead research, outreach preparation and sales follow-up.

Builds or configures AI workflows for operations, supply chain, logistics, procurement and fulfilment.

Builds or configures AI assistance for customer service, success and support.

Maps business requirements, records and decisions for an AI project.

Designs interfaces and interactions through which people use AI systems.

Helps customers use and review AI in their work after implementation.

Produces, edits and reviews content with AI tools.

Enterprise AI leader responsible for the organization's AI priorities and governance.

Leads AI work for a function or organization.

Enterprise AI leadership at the VP/SVP level, typically running the AI platform, AI product, or AI-X function (AI Security, AI Strategy, AI Innovation).

Operational AI ownership, often the role accountable for shipping AI features and AI-touching products in a specific business unit.

Sets AI governance policy and coordinates risk review across the organization.

Explains AI capabilities through demonstrations, technical content and workshops.

Uses natural-language instructions and AI coding tools to build software.

Coordinates agents, tools and human decisions across a workflow.

Checks AI outputs and agent actions against defined criteria.

Defines what an AI product should help users do and which decisions remain with its owners.

Develops AI priorities with business and technical owners.

Coordinates the delivery and introduction of AI initiatives across teams.

Helps an organization define an AI problem, compare approaches and plan the work.

Helps teams introduce AI into agreed responsibilities.

Monitors agent queues, outputs, failures and escalations after implementation.