Hire Generative AI & Agentic AI Engineers

They work in LangGraph and LangChain, and they think in terms of evaluation harnesses, token budgets, grounding and failure modes rather than prompt tricks. Expect people who will tell you when a deterministic workflow would beat an agent, because that judgement is what keeps these projects from quietly failing.

Before committing to a build, expect these engineers to ask what good looks like and how you will measure it. Generative systems degrade quietly — a retrieval step drifts, a prompt regresses, a model version changes underneath you — and without an evaluation harness nobody notices until a customer does. Teams that start with that harness ship slower in week one and considerably faster by month three.

Typical stack

What they do

• Design retrieval-augmented generation pipelines over private document collections
• Build multi-agent and graph-orchestrated workflows with checkpointing and recovery
• Add human-in-the-loop approval gates where autonomous action is too risky
• Set up evaluation harnesses so prompt and model changes are measured, not guessed
• Control inference cost and latency through caching, routing and model selection
• Integrate LLM features into existing enterprise APIs and permission models

Use cases we deliver

Related specialisms

Frequently asked questions