Start with the business workflow
“We need AI engineers” is too broad to be a useful requisition. Begin with the workflow to improve, the data required, the consequence of failure, and the systems involved. An internal knowledge assistant and an agent that can modify customer accounts have different engineering and governance needs.
Four roles and their boundaries
Data engineers make information reliable and accessible. Applied AI engineers deliver model-enabled product capabilities and measure their quality. Agentic AI engineers build controlled execution across tools and systems. Forward Deployed Engineers adapt and integrate technology in complex customer environments.
These are capabilities rather than rigid job classifications. One experienced engineer may cover multiple responsibilities in a small team; at larger scale, clear specialization can reduce bottlenecks.
Three common team patterns
Pattern A: Knowledge product
Start with applied AI engineering and dependable document ingestion. Existing backend engineers may own identity, APIs and user experience. Bring dedicated data engineering support when source systems, access controls or freshness require ongoing ownership.
Pattern B: Enterprise rollout
Pair applied AI capability with a Forward Deployed Engineer who can handle each customer's identity systems, external APIs, data mappings and production deployment concerns. Feed recurring integration issues back into the core product.
Pattern C: Multi-system agent
Add explicit agentic capability for durable workflows, action authorization, tool reliability and evaluation. Existing platform engineering should still own infrastructure, incident response and the operating model where practical.
Prioritize the first hire
- Is source data unreliable? Hire or allocate data engineering first.
- Is the application itself not yet production ready? Prioritize applied AI and conventional backend engineering.
- Does the product work but customer deployment fails? Consider a Forward Deployed Engineer.
- Will models execute side-effecting actions? Add agentic AI design and security competence early.
Define operating interfaces
For each capability, document the owned repository or modules, data contracts, APIs, deployment responsibility, quality thresholds and escalation path. A team without clear boundaries often creates the appearance of speed while pushing failures into integration and operations.
See our dedicated AI teams page for an embedded engagement model and technical assessments for each specialty.