What they handle

• Translate approved use cases into application requirements.
• Integrate AI services, model APIs, and application components.
• Develop user-facing or internal AI-enabled features.
• Implement input validation, error handling, and usage controls.
• Test outputs across representative scenarios and edge cases.
• Document architecture, dependencies, limitations, and operating steps.

What they handle

• Map current processes, inputs, decisions, and system handoffs.
• Build approved automations and system integrations.
• Connect APIs, databases, business tools, and workflow platforms.
• Implement logging, exception handling, and notification steps.
• Test automations before deployment and monitor early performance.
• Maintain technical documentation and change records.

What they handle

• Prepare and validate data used for model development.
• Build or adapt models according to approved requirements.
• Evaluate model behavior using defined performance measures.
• Support deployment, monitoring, and version-management workflows.
• Investigate model drift, data changes, or performance issues.
• Document assumptions, dependencies, testing, and known limitations.

What they handle

• Clarify the workflow, user need, and expected outcome.
• Assess integration requirements and technical dependencies.
• Prototype solution approaches using approved tools and services.
• Coordinate implementation across application, data, and business teams.
• Define testing, monitoring, and human-review requirements.
• Prepare technical documentation and implementation guidance.