AI engineer job description: a practical template for your next hire

What should an AI engineer job description include?

An AI engineer job description should explain the product the person will build, the outcomes they will own, and the technical judgment the job requires. For an applied AI role, that usually means integrating models into software, evaluating the results, and making the system dependable enough for real users.

Start there. A list of model names and frameworks is not a hiring brief.

My advice to founders is to hire for potential rather than pedigree, and this is where that starts. If your posting demands a famous employer, a research degree, and every AI tool you have heard of, you have described a background. You still have not described the work.

The template below is for an applied AI engineer building product features with existing models. It is an illustrative starting point, not a real client opening. Replace every bracketed field before publishing. If the actual job is training proprietary models, building a data platform, or leading research, change the role before changing the title.

Which AI engineering role are you actually hiring?

Still deciding whether this is an applied AI or modeling role? Start with the AI engineer vs. ML engineer hiring comparison, then return here to write the posting.

Define the business problem before you choose the title. An internal knowledge assistant, a recommendation system, and a new model architecture can require different people, even when all three projects are called AI.

Google’s Professional Machine Learning Engineer guide covers building, evaluating, productionizing, and optimizing AI solutions, including work with foundation models. That breadth is a useful warning: the words AI and ML in a title do not give you a reliable boundary. Write down the deliverable and the responsibilities instead.

Use this table to sharpen the brief. These are suggested hiring distinctions, not universal job classifications.

If the immediate work is…	Start by scoping…	Evidence to ask for
Add an assistant, document workflow, or model-powered feature to an existing product	Applied AI engineer	A shipped feature, evaluation approach, failure handling, and integration decisions
Develop and operate models using proprietary training data	Machine learning engineer	Data preparation, experiments, model evaluation, deployment, and monitoring
Build the shared infrastructure that supports model teams	ML platform or infrastructure engineer	Reliable pipelines, deployment systems, observability, access controls, and operational ownership
Explore new methods where the model itself is the product	Research-focused engineer or scientist	Relevant experiments, research contributions, and a clear connection to the problem

If you need the broader search process, read the guide to hiring AI and ML engineers. This page focuses on turning the role you have chosen into a usable job description.

Can you copy this AI engineer job description template?

Yes. Copy the sections below, then make them specific to your company. Remove responsibilities you cannot explain, name the support available, and replace aspirational requirements with evidence a candidate can actually demonstrate.

Job title and working arrangements

Applied AI Engineer, [level]

Location: [eligible locations and remote, hybrid, or on-site expectations]

Compensation: [approved salary range, currency, and any separately described bonus or equity]

Reporting to: [manager and role]

Team: [people the engineer will work with and the support already in place]

Travel or schedule expectations: [specific requirements, if relevant]

About the work

We are building [product] for [users]. Our next problem is [specific workflow or customer need]. We are hiring an applied AI engineer to turn [current starting point] into [useful product outcome], working with [engineering, product, design, data, or security partners].

This role uses [existing models or model providers] within a software product. You will own [defined part of the system], including how we evaluate its behavior and handle failures. You will not be expected to own [research, all infrastructure, or another responsibility outside the scope].

What you will do

• Build and maintain [specific model-powered feature or workflow] in [existing product or service].
• Connect models to approved data sources and application tools, with appropriate access boundaries.
• Create evaluation examples based on real user tasks, including expected behavior and important failure cases.
• Test quality, response time, and operating cost before and after changes.
• Work with engineering partners on deployment, logging, monitoring, and incident handling.
• Document technical decisions and explain tradeoffs to product and business teammates.
• Improve the system using user feedback, observed failures, and agreed product measures.

What success looks like

During your first 30 days, you will understand [workflow], review [available data and systems], and agree on the first delivery milestone with [owner]. You will identify important risks and establish an initial evaluation set.

By 60 days, you will deliver [bounded pilot] to [defined user group], with documented limitations and a way to collect feedback. The team will have evidence about whether the approach is useful enough to continue.

By 90 days, you will help the team decide whether to expand, revise, or stop the pilot. For an expansion, you will document operational ownership, release criteria, monitoring, and the next engineering priorities.

These milestones are planning placeholders. Adjust them to the state of your product and the resources available. They are not a promise that every AI project should reach production in a quarter.

Required experience

• Experience building and maintaining software in [the language the role genuinely needs].
• Evidence of integrating an AI or ML capability into a usable application, through professional work or a substantive project.
• Ability to explain how you evaluated the system and what happened when it failed.
• Familiarity with APIs, testing, data handling, version control, and the deployment practices relevant to our environment.
• Ability to reason about access permissions, sensitive information, and the boundaries of model-generated output.
• Clear communication about uncertainty, tradeoffs, and decisions.

Helpful experience

Experience with [relevant domain], [specific retrieval or evaluation tooling], or [our deployment environment] would help. Equivalent experience with another stack is welcome where the underlying engineering skills transfer.

Keep this list short. A requirement belongs here only when it will materially change the person’s ability to do this job.

How we will interview

Our proposed process is [screen], [job-relevant technical discussion or exercise], [team collaboration conversation], and [final alignment]. We will tell you the expected time, preparation, participants, and decision schedule before you begin.

Please do not share confidential work from a current or former employer. We can use a public project, a sanitized example, or a supplied scenario. Contact [person or address] to discuss access needs or an alternative assessment format.

How to apply

Send [application materials] to [application destination]. Include a short example of something you built, the part you owned, and one decision you would approach differently now. A public portfolio is helpful if you have one, but is not a substitute for explaining your contribution.

How do you make the template specific enough to attract the right engineer?

Replace broad ambitions with a bounded problem, a known starting point, and an honest account of what the team can support. A strong candidate needs to understand the job they are accepting, not just the technology they might touch.

Here is an illustrative rewrite:

Too vague: “Build cutting-edge AI solutions using LLMs, agents, machine learning, and advanced analytics.”

More useful: “Build an internal support assistant that retrieves approved documentation, links answers to their sources, and routes uncertain requests to the support team. Work with our backend lead and support manager to evaluate answer quality before expanding the pilot.”

The second version leaves room for engineering judgment. It also gives the candidate enough information to ask worthwhile questions. Which documents are approved? Who can access them? What should the assistant refuse to answer? What does useful mean to the support team?

I would rather see those questions than a confident promise to build everything immediately. The point of a clear brief is to make judgment visible. For the wider writing principles, use the engineering job-description guide.

Which responsibilities should you include for production AI?

Include evaluation, data boundaries, operational monitoring, and an owner for failure handling when the role will ship a live system. A prototype that produces an impressive answer is a different deliverable from a feature people can rely on.

The NIST Generative AI Profile describes risks including confabulation, privacy, and information security. That does not mean your engineer personally becomes the legal, security, and policy department. It means the job description should say who they work with and which decisions they own.

For retrieval-based systems, define who owns document ingestion, freshness, permissions, and source attribution. Microsoft’s retrieval-augmented generation overview explains how retrieval supplies grounding information to a model. Adding retrieval does not remove the need to evaluate the resulting answers.

For tool-using systems, explain which actions need approval and which tools are out of scope. The OWASP Top 10 for LLM Applications includes prompt injection, sensitive information disclosure, and excessive agency. Those are concrete reasons to ask about boundaries rather than simply requesting “agent experience.”

Make the evaluation responsibility equally concrete. “Improve model quality” is vague. “Maintain representative test cases, investigate failures, and agree release criteria with the product owner” describes work. Anthropic’s evaluation guidance starts with clear success criteria and relevant tests. The hiring question is whether a candidate can apply that discipline to your product, regardless of vendor.

What should you remove from an AI engineer job description?

Remove requirements that describe prestige, duplicate another person’s job, or demand expertise unrelated to the immediate work. Every extra filter should earn its place.

I come back to the same point in engineering hiring: potential matters. A person who can explain a difficult production tradeoff may be more relevant than someone who lists every fashionable framework but cannot describe their own contribution.

Instead of requiring…	Ask for evidence of…
Every model provider and orchestration framework	Learning an unfamiliar API and choosing an appropriate integration approach
A research degree for an application integration role	The technical depth needed for the actual work
A famous technology company on the resume	Ownership, problem solving, and useful shipped software
One person owning research, data, infrastructure, security, and product	A defined contribution and clear collaboration boundaries
”Expert prompt engineer” without context	A reproducible way to evaluate and improve system behavior

Do not turn flexibility into ambiguity. If a particular language, deployment environment, or on-call obligation is genuinely essential, say so. The aim is an honest bar, not an artificially easy one.

How should the interview connect to the job description?

Every assessment should test a responsibility you actually listed. If the brief says the person will ship and evaluate AI features, ask them to reason through a small feature, its failures, and its release criteria.

For example, give candidates the same fictional support-assistant scenario. Ask what they would build first, how they would select evaluation examples, what data they would exclude, and what would make them stop a release. A candidate can explain the reasoning without building your company’s feature for free.

Use a consistent rubric and let the technical interviewer assess the depth. I recommend keeping the whole process within the four-step interview framework. A well-scoped conversation should replace a redundant round, not become another stage layered onto an already long process.

At Kruger Recruiting, I can help turn that brief into an AI and machine learning recruiting search. The useful starting point is what the hire needs to deliver, with enough context to identify the right experience.

What should you confirm before opening the search?

Confirm the approved compensation, location, manager, available data, team support, and decision-maker. Then have the people who will interview the candidate read the same brief.

If the founder expects a first product builder while the technical lead expects a research specialist, resolve that before outreach. If this will also be your first engineering employee, use the founding-engineer guide to clarify ownership and the shape of the offer. Do not hide a founding role inside an ordinary specialist job description.

My final check is simple: could a qualified person read this posting and explain what they would own, who would help them, and how the team would decide whether the work was useful? If the answer is no, revise the brief before collecting resumes.

If you want a second opinion on the role, book a free strategy call. Bring the draft and the business problem. We can start with the work.

Sources and further reading

• Google Cloud: Professional Machine Learning Engineer
• NIST: Generative Artificial Intelligence Profile
• Microsoft Learn: Retrieval-augmented generation overview
• OWASP: Top 10 for Large Language Model Applications
• Anthropic: Develop test cases

Frequently Asked Questions

What is an AI engineer job description?

An AI engineer job description defines the outcomes, responsibilities, and experience required to build or operate AI-enabled systems. For an applied role, specify the product, model integrations, evaluation responsibilities, operational support, and boundaries of ownership. The title alone does not establish whether the person will integrate existing models or develop new ones.

Does an applied AI engineer need a PhD?

Do not require a PhD by default for an application-focused role. Decide which knowledge the work requires and how candidates can demonstrate it. Research-heavy work may call for different qualifications than integrating models into an existing software product.

Which programming languages should the job description require?

Name the language the engineer actually needs to use in your environment, and explain where it fits. Separate essential day-one requirements from tools someone can learn. A long language list makes it harder to understand the real technical bar.

Should the AI engineer own evaluation and monitoring?

Name their contribution to both when the role ships a live feature. Clarify who defines product success, who maintains evaluations, and who responds to failures. Do not imply that one engineer replaces the product, infrastructure, or security team.

How is this different from a machine learning engineer job description?

This template assumes the person integrates existing models into a product. A machine learning role may put more emphasis on training data, experiments, model development, and model operations. Titles overlap, so describe the actual deliverable and responsibilities rather than relying on the label.

Can a startup use this template for its first AI hire?

Yes, after adjusting it for the product stage and available support. Be explicit if the hire must also establish engineering foundations or act as a founding engineer. Replace the sample milestones with a plan the company can realistically resource.