This free AI 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-powered features using foundation models — LLMs, embeddings, retrieval (RAG), and agents — rather than training models from scratch. It's a software engineering role with applied AI depth, and one of the fastest-growing roles in software.
AI engineering candidates want to know what you're building (chat, search, agents, copilots), which models and frameworks you use, and how mature the work is. Be specific, because 'AI Engineer' spans everyone from prompt tinkerers to serious systems builders.
If the role is mainly training and validating models, use the Machine Learning Engineer or Data Scientist template instead.
Writing tips
- Describe what you're building with AI (chat, RAG, agents, copilots) and the models you use.
- Clarify the line between this role and ML engineering or data science.
- Emphasize software engineering fundamentals — AI features are still production systems.
- Mention evaluation: strong AI engineers care about measuring quality, not just shipping demos.
- 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]. AI is central to [feature/area], and we're hiring an AI Engineer to build features on top of foundation models that our users rely on.
The role
As an AI Engineer, you'll build product features powered by LLMs and other foundation models — designing prompts and retrieval, wiring up agents and tools, and shipping reliable AI experiences. You'll care as much about evaluation and latency as about the demo. This role reports to {{hiring_manager}} and is based {{work_type}} in {{location}}.
What you'll do
- Build AI-powered features using LLMs, embeddings, and retrieval (RAG).
- Design, test, and iterate on prompts, tools, and agent workflows.
- Build evaluations to measure and improve output quality.
- Optimize for latency, cost, and reliability in production.
- Partner with product and design to turn AI capabilities into real features.
What we're looking for
- 3+ years of software engineering, with hands-on experience building AI features.
- Proficiency in [Python / TypeScript] and experience with LLM APIs or frameworks.
- Practical understanding of prompting, RAG, and evaluation.
- A focus on shipping reliable systems, not just prototypes.
- Curiosity to keep up with a fast-moving field.
Nice to have
- Experience with agent frameworks or tool-calling.
- Familiarity with vector databases and embeddings at scale.
- Exposure to fine-tuning or model evaluation.
What we offer
- Salary range: {{salary_range}}, plus equity.
- [Comprehensive benefits].
- Flexible {{work_type}} working and [PTO policy].
- The chance to build at the frontier of applied AI.
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 Engineer do?
- An AI Engineer builds product features on top of foundation models — using LLMs, embeddings, retrieval (RAG), and agents. They design and evaluate prompts and workflows, integrate model APIs, and ship reliable AI experiences, focusing on applying models rather than training them from scratch.
- What's the difference between an AI Engineer and a Machine Learning Engineer?
- An AI Engineer builds applications on top of existing foundation models (LLMs and APIs), focusing on prompting, retrieval, agents, and product integration. A Machine Learning Engineer builds and deploys models themselves — training pipelines, serving, and MLOps. AI engineering is more application-focused; ML engineering is more model-focused.
- What skills should an AI Engineer have?
- Strong software engineering fundamentals, proficiency in Python or TypeScript, hands-on experience with LLM APIs and frameworks, and a practical understanding of prompting, RAG, and evaluation. A focus on shipping reliable systems — and measuring their quality — matters more than novel research.