New Roles · Engineering AI Builders

Prompt Engineer. The role, the market signal, and how to build it in your org.

Designs, tests, and maintains prompt suites and evals. Often paired with agent-prompt and red-team teams at model labs.

In a workforce of people and AI agents, this role takes shape around the workflows it supports. Define its responsibilities and handoffs, then prepare people to perform that work.

Source snapshot 2026-07-17. Postings do not confirm that a vacancy is still open.

13 postings · 10 distinct titles · from 264,613 real job postings · see the source snapshot →

What the market calls it
Prompt EngineerPrompt Engineer, EvalsLLM Specialist
Employers in this source snapshot
Accenture 3Gevernova 2Cognizant 1Adobe 1Anthropic 1

What the postings ask this role to do

221 tasks extracted from real Prompt Engineer job descriptions, classified Automate / Augment / Human-only. In this source classification, 3.6% of task statements are automation candidates. The share does not measure working time or demonstrated agent performance.

Automate
  • Conduct unit tests on developed code.
  • Document prompt engineering processes.
  • Document prompt engineering best practices.
  • Execute source code for software applications.
  • Maintain reusable prompt libraries and version control.
Augment
  • Employ techniques to guide and enhance model responses
  • Collaborate with domain experts to understand business needs and translate them into clear and effective llm prompts.
Human-only
  • Make team decisions
How we build it in your org

From the market's version of this role to your version of it

1. Define workflow responsibilities
Start with the actual work this role must improve and the output your enterprise needs. Define how people and agents share the decisions, exceptions and handoffs. Task Intelligence examines the tasks within that work.
2. Define your version
Your team defines the role around its own systems, data, rules and responsibilities. Agree what the person configures, builds, reviews or supervises and what technical support is needed.
3. Practise and assess readiness
Build on the domain and technical expertise your people already bring. Shape a practical project around your enterprise context, then use relevant Simulations, GenAI Sandboxes and Skill Validation Assessments to review the work and capability demonstrated.

Start with the work, not the org chart.

Define what this role will build, configure, decide and check. Connect that work to an enterprise-specific project and agreed assessment evidence.