This free AI Agent Engineer job description template is ready to use — copy it, replace the {{placeholders}}, and post your role in minutes. It includes a company intro, a role summary, responsibilities, requirements, nice-to-haves, and compensation, with writing tips and FAQs below to help you tailor it to your team.
When to use this template
Use this when you're hiring an engineer to build AI agents — systems that use LLMs to plan, call tools, and complete multi-step tasks with some autonomy. It's a newer, fast-moving specialty within applied AI.
Agent engineering candidates want to know what your agents do, how autonomous they are, and how you handle reliability and guardrails. Be specific, because agents are powerful but hard to make dependable.
If the role is general AI feature work, use the AI Engineer template; if it's deep model work, use the LLM Engineer template.
Writing tips
- Describe what your agents do and how much autonomy they have.
- Emphasize reliability, guardrails, and evaluation — agents are hard to make dependable.
- Clarify the tools, frameworks, and integrations involved.
- Distinguish from general AI engineering and deep model work.
- Include the salary range and seniority level.
The job description
Copy the template below and replace the {{placeholders}} and [bracketed notes] with your specifics.
About {{company}}
{{company}} is [what you do]. We're building AI agents that [what they do], and we're hiring an AI Agent Engineer to make them capable and reliable.
The role
As an AI Agent Engineer, you'll build agents that use LLMs to plan, call tools, and complete multi-step tasks. You'll design the workflows, wire up tools and memory, and do the hard work of making agents reliable enough to trust. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Design and build agent workflows — planning, tool use, and memory.
- Integrate the tools and APIs agents need to get work done.
- Build guardrails and evaluations to make agents reliable and safe.
- Debug the failure modes unique to autonomous, multi-step systems.
- Partner with product to turn agent capabilities into real features.
What we're looking for
- 3+ years of software engineering, with hands-on agent or LLM experience.
- Strong Python or TypeScript and experience with agent frameworks.
- A practical understanding of tool use, planning, and evaluation.
- Patience and rigor for making non-deterministic systems reliable.
- Curiosity about a fast-moving field.
Nice to have
- Experience shipping agents to production.
- Familiarity with orchestration frameworks and vector stores.
- A background in distributed or workflow systems.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- A chance to build agents at the frontier of what's possible.
How to personalize
Replace these placeholders before posting:
- {{company}}
- {{location}}
- {{work_type}}
- {{salary_range}}
- {{hiring_manager}}
The bracketed notes — like [your benefits] or [your primary language(s)] — are prompts to swap in your own details. The more specific you are about the actual work and stack, the stronger your applicant pool will be.
Frequently asked questions
- What does an AI Agent Engineer do?
- An AI Agent Engineer builds AI agents — systems that use LLMs to plan, call tools, and complete multi-step tasks with some autonomy. They design agent workflows, integrate tools and memory, build guardrails and evaluations, and do the hard work of making agents reliable enough to trust.
- What's the difference between an AI Agent Engineer and an AI Engineer?
- An AI Engineer builds AI features broadly, often single-step (a prompt in, a response out). An AI Agent Engineer specializes in autonomous, multi-step systems that plan and use tools — which adds significant challenges around reliability, guardrails, and debugging non-deterministic behavior.
- What skills should an AI Agent Engineer have?
- Strong software engineering fundamentals in Python or TypeScript, hands-on experience with agent frameworks and tool use, a practical understanding of planning and evaluation, and the patience and rigor to make non-deterministic, multi-step systems reliable.