A good AI engineer job description tells candidates exactly what they will build, which models and tools they will use, and what “production ready” means on your team. AI engineers design, build and deploy applications powered by machine learning and large language models, so the job description should focus on shipping real features, not just research. Below is a copy and paste template, plus the duties, skills and salary data you need to write one that attracts the right people.
Key takeaways
- An AI engineer builds and ships AI powered products. The role sits between software engineering and machine learning.
- The best job descriptions separate a short list of required skills from preferred skills, so strong candidates do not screen themselves out.
- AI engineers are not the same as ML engineers or data scientists. Mixing the three roles in one posting attracts the wrong applicants.
- BLS has no separate AI engineer category. Software developers (May 2025 median of $135,980) and computer and information research scientists (May 2025 median of $140,300) are the closest benchmarks.
- Include a salary range, a clear tech stack and one or two real problems the hire will work on.
What should an AI engineer job description include?
Every strong AI engineer job description answers five questions: what the company does, what the engineer will build, which tools and models they will use, what experience is truly required, and what the role pays. If a candidate cannot answer those after reading your posting, they will either skip it or apply without the right background.
Keep the posting specific. “Work on cutting edge AI” tells a candidate nothing. “Build a RAG based assistant that answers support tickets using our product documentation” tells them exactly what they will do. For more on structure and tone, see our guide on how to write a great job posting.
Recruiter tip: When we review AI job postings with clients, the single most common problem is a requirements list that reads like a wish list. If you list 15 “required” frameworks, experienced engineers assume the team does not know what it needs. Pick the five that truly matter and move the rest to preferred.
Is there an AI engineer job description template I can copy?
Yes. Use the template below as a starting point, then replace the bracketed text with your own details. Adjust the years of experience and the tech stack to match the level you are hiring for.
AI Engineer job description template (copy, paste and edit the bracketed text)
Job title: AI Engineer [or Senior AI Engineer]
Location: [City, State / Hybrid / Remote]
Employment type: [Full time / Contract]
Salary range: $[minimum] to $[maximum], plus [bonus, equity, benefits]
About us: [Two or three sentences on what your company does, who your customers are and why AI matters to your product.]
About the role: We are looking for an AI Engineer to design, build and ship AI features that solve real problems for [customers or internal teams]. You will work with product managers, software engineers and data teams to take models from prototype to reliable production systems.
What you will do:
- Build and ship features powered by large language models (LLMs) and other machine learning models
- Design retrieval augmented generation (RAG) pipelines, prompts and agent workflows
- Evaluate model quality with automated tests, benchmarks and human review
- Deploy, monitor and improve AI services in production with attention to cost, latency and reliability
- Integrate models and third party AI APIs into our [web, mobile or backend] systems
- Apply responsible AI practices for privacy, security, bias and safety
- Document your work and share what you learn with the wider engineering team
What you bring (required):
- [X]+ years of professional software engineering experience, including production AI or ML work
- Strong Python skills and experience with at least one ML framework such as PyTorch or TensorFlow
- Hands on experience building applications with LLMs, embeddings and vector search
- Experience deploying services on [AWS, Azure or GCP] using containers and CI/CD
- Solid understanding of evaluation, testing and monitoring for AI systems
Nice to have (preferred):
- Experience fine tuning or adapting open source models
- MLOps tooling experience such as MLflow, Kubeflow or similar
- Background in [your domain: healthcare, manufacturing, finance, etc.]
- Degree in computer science, engineering, math or a related field, or equivalent experience
How to apply: [Application link or instructions]. [Company] is an equal opportunity employer.
Before you post, check the language. The EEOC notes that a job ad showing a preference for, or discouraging applicants based on, protected traits such as age can violate the law. Phrases like “recent college graduates” or “digital native” can create that risk, so describe the skills you need instead.
What are the main responsibilities of an AI engineer?
The day to day work varies by company, but most AI engineers spend their time in five areas.
Building AI features
AI engineers turn a model into something users can actually use. That means writing application code, designing prompts and agent workflows, building retrieval pipelines that ground models in company data, and handling edge cases when the model gets it wrong.
Evaluating model quality
Unlike traditional software, AI output is not fully predictable. AI engineers build evaluation sets, automated checks and human review loops to measure accuracy, relevance and safety before and after every change.
Deploying and monitoring in production
Shipping is where many AI projects stall. AI engineers package models and services, deploy them to the cloud, and monitor cost, latency, errors and drift once real users arrive.
Working across teams
AI engineers work with product managers to decide what to build, with software engineers to integrate features, and with data teams to access clean, well governed data.
Managing risk
Responsible AI is now part of the job. Many teams use the NIST AI RMF, a voluntary framework for managing risks to individuals, organizations and society from AI, to guide how they design, test and monitor AI systems.
Which AI engineer skills are required and which are preferred?
Separating must haves from nice to haves widens your candidate pool without lowering the bar. Here is how we typically advise clients to split them for a mid level AI engineer.
| Skill area | Required | Preferred |
|---|---|---|
| Programming | Python, software engineering fundamentals, APIs, testing | TypeScript, Go or Java for production services |
| Machine learning | Core ML concepts, one framework such as PyTorch or TensorFlow | Training or fine tuning models from scratch |
| LLM applications | Prompt design, embeddings, vector search, RAG | Agent frameworks, tool use, multi model orchestration |
| Evaluation | Building test sets and automated evaluations | Experience designing human review programs |
| Cloud and deployment | One major cloud, containers, CI/CD | Kubernetes, GPU infrastructure, cost optimization |
| MLOps | Logging and monitoring for models in production | MLflow, Kubeflow or similar platforms |
| Responsible AI | Awareness of privacy, security and bias risks | Hands on work with a formal AI risk framework |
| Education | Degree in a related field or equivalent experience | Graduate degree for research heavy roles |
Our earlier guide on what an AI engineer is and the key skills to look for goes deeper on how to assess each of these areas during hiring.
How is an AI engineer different from an ML engineer or a data scientist?
These titles overlap, and many companies use them loosely. Getting the distinction right in your job description is one of the easiest ways to attract the right applicants.
| AI engineer | ML engineer | Data scientist | |
|---|---|---|---|
| Main goal | Ship AI powered product features | Build and scale ML training and serving systems | Find insights and build predictive models from data |
| Typical work | LLM apps, RAG, agents, integrations, evaluations | Feature pipelines, model training, deployment infrastructure | Analysis, experiments, statistical models, dashboards |
| Core tools | Python, LLM APIs, vector databases, cloud services | Python, PyTorch or TensorFlow, Spark, Kubernetes, MLOps tools | Python or R, SQL, notebooks, BI tools |
| Output | A working feature in production | Reliable models and platforms other teams use | Recommendations, forecasts and prototypes |
| Closest background | Software engineering | Software engineering plus ML | Statistics, math or analytics |
If your role is mostly about integrating foundation models into products, call it an AI engineer. If it is mostly about training pipelines and infrastructure, call it an ML engineer. We cover how these roles are changing team structures in the rise of AI engineers.
How much does an AI engineer make?
The Bureau of Labor Statistics does not publish a separate occupation for AI engineers. Depending on the work, AI engineers are usually counted as software developers or, for research heavy roles, as computer and information research scientists. We use both as benchmarks and say which is which.
| BLS occupation (May 2025) | Median annual pay | Lowest 10% earned less than | Highest 10% earned more than |
|---|---|---|---|
| Software developers | $135,980 | $82,460 | $214,670 |
| Computer and information research scientists | $140,300 | $82,200 | $230,630 |
For most applied AI engineer roles that build product features, the software developer figures are the better fit. For roles focused on new algorithms and research, the research scientist figures apply, and BLS notes those workers typically need at least a master’s degree.
Outlook is strong for both. BLS projects software developer, quality assurance analyst and tester employment to grow 10 percent from 2025 to 2035 and cites expanding AI software development as a driver. Computer and information research scientist employment is projected to grow 22 percent over the same period. Real offers for experienced AI engineers in competitive markets often land well above the medians, so benchmark against your own market and include a clear range in the posting.
What interview questions should you ask an AI engineer?
The best questions test whether a candidate has actually shipped AI into production, not just experimented with it. Here are questions our recruiters recommend:
- Walk me through an AI feature you took from prototype to production. What broke after launch, and how did you fix it?
- How do you decide whether to use retrieval, fine tuning or better prompting to improve a model’s answers?
- How do you evaluate an LLM feature before release? What metrics and test sets do you use?
- Tell me about a time a model produced harmful, biased or wrong output. How did you detect it and what did you change?
- How would you reduce the cost and latency of an AI feature that is getting expensive at scale?
- Describe how you work with product managers when requirements for an AI feature are unclear.
Pair these with structured behavioral questions so you can compare candidates fairly. Our lists of questions to ask an interviewee and behavioral interview questions are good companions.
Recruiter tip: Add a short, realistic take home or live exercise, such as improving a simple RAG pipeline, and keep it under two hours. Strong AI engineers often have several offers, and long unpaid assignments are one of the fastest ways to lose them.
Frequently asked questions
Does an AI engineer need a master’s degree or PhD?
Not for most applied roles. Many AI engineers have a bachelor’s degree in computer science or a related field and strong software engineering experience. Graduate degrees matter more for research roles, where BLS says computer and information research scientists typically need at least a master’s degree.
What is the difference between an AI engineer and a prompt engineer?
Prompt design is one skill within AI engineering, not usually a standalone job. An AI engineer also builds the retrieval systems, evaluation tooling, integrations and production infrastructure around the prompts.
How many years of experience should I require for an AI engineer?
Focus on relevant software engineering experience rather than years of “AI” experience, since modern LLM tooling is relatively new. For a mid level role, a few years of production software work plus hands on AI project experience is a reasonable bar.
Should I hire a full time AI engineer or a contractor?
If AI is core to your product roadmap, a full time hire builds lasting knowledge. If you need to prove out one use case quickly, a contract engineer can help you move faster while you decide on a long term team.
Where should I post an AI engineer job description?
General job boards bring volume, but many strong AI engineers are not actively applying. Referrals, technical communities and specialized recruiters usually reach more qualified passive candidates.
Need help hiring an AI engineer who can actually ship? Our software developer recruiters source and screen AI, ML and software engineers for teams across the country. Tell us about your open role and we will help you build a shortlist.
