AI engineer vs. ML engineer: which should you hire first?

AI engineer vs. ML engineer: what is the difference for hiring?

For hiring purposes, an applied AI engineer usually focuses on building useful software around existing models. A machine learning engineer often focuses on developing, evaluating, deploying, and maintaining models and their data pipelines. The responsibilities overlap, so choose your first hire by the problem they need to solve, not by the title that sounds more current.

My advice is to finish this sentence before opening a search: “The person we hire needs to make ___ work for ___, and we will know it works when ___.” If your leadership team gives three different answers, you have a scope problem to resolve before you have a recruiting problem.

I care about this because expectation and reality need to match. That is one of the recurring themes in my advice on hiring: a capable person can still be the wrong hire for the work you actually need done.

This guide is for founders and hiring managers choosing their first dedicated AI or ML hire. It is a decision guide, not a universal definition of either profession. The scenarios below are illustrative, not client case studies.

Why are AI and ML job titles so confusing?

There is no clean industry-wide boundary between these titles. Machine learning is part of artificial intelligence, and employers use both labels for a broad range of work. Here, I use “applied AI engineer” specifically for a product-oriented role building with existing models, including large language models.

Even that distinction has limits. Google’s Professional Machine Learning Engineer description includes conventional ML, foundation models, application development, deployment, and monitoring. It is a useful example of how much one title can cover, not a requirement that every candidate must hold that certification.

That means two mistakes are easy to make. You can reject a qualified ML engineer because their title does not say AI. Or you can hire someone with an AI title whose strongest experience is experimentation when you need someone to operate customer-facing software.

My recruiting preference is to look past the label and pedigree. Ask what the person built, what they personally owned, and what happened when it did not work. Those answers give you something to evaluate.

For the wider search process, compensation considerations, and candidate signals, use my guide to hiring AI and ML engineers. Here, the narrower question is which responsibility deserves your first headcount.

How do the responsibilities compare?

Use this comparison to scope a role, then validate it with your technical lead. These are common centers of responsibility, not rigid boundaries. Either role may need strong software engineering, data judgment, evaluation skills, and production experience.

Hiring question	Applied AI engineer emphasis	ML engineer emphasis
What needs to improve?	A workflow or product feature powered by existing models	A predictive or learned system and the pipeline supporting it
What might they deliver?	A document assistant, extraction workflow, or model-powered product capability	A forecasting, ranking, classification, or other trained model in production
Where is much of the engineering work?	Application integration, retrieval, evaluation, access controls, latency, and failure handling	Data preparation, experiments, training, deployment, monitoring, and retraining
What evidence should I request?	A shipped feature and an explanation of quality, cost, and operational tradeoffs	A model lifecycle example, including validation and production behavior
What should I clarify before hiring?	Product owner, model access, permitted data, user workflow, and release criteria	Data ownership, useful labels or feedback, baseline, infrastructure, and model ownership
What is an unfair expectation?	One person independently owning product, security, platform, and all AI research	One person fixing missing data foundations while also delivering a mature model system immediately

The most useful row is often the last one. An ambitious role is fine. A role with unlimited accountability and no support is a different proposition. Tell candidates which decisions they can make and which dependencies they will inherit.

Which engineer should you hire for your actual product?

Start with the obstacle between your team and a useful outcome. The following examples show how the same broad ambition, “we want to use AI,” can lead to different hiring decisions.

Scenario 1: You want a reliable assistant inside an existing product

Imagine a software company wants customers to ask questions about their own documents. The application already exists, and the team plans to use an external language model. The difficult work is connecting the feature to the product, retrieving appropriate information, evaluating answers, and respecting user permissions.

I would start by scoping an applied AI engineer with strong software engineering experience. I would not make training a model from scratch a prerequisite unless the technical plan actually requires it.

Microsoft’s retrieval-augmented generation overview explains how RAG grounds model responses in external content. It also identifies practical challenges including retrieval quality, access control, and response time. That is why a convincing chat demo is only part of the evidence you need from a candidate.

Your brief should say who owns document ingestion and permissions. It should also say what happens when the system cannot answer. If those decisions are unresolved, give the new hire authority and support to resolve them rather than silently treating them as completed work.

Scenario 2: Your product depends on better predictions from proprietary data

Now imagine a business with historical transactions that wants to improve demand forecasts. It needs a useful baseline, a defensible way to evaluate predictions, and a repeatable process for serving and updating the model.

I would explore an ML engineering role, with the exact balance of modeling and infrastructure determined by the existing team. The candidate needs to reason about the data and evaluation design, not simply name a preferred algorithm.

Before opening the search, ask your technical lead whether the historical data is accessible and suitable for the proposed task. What outcome are you predicting? What information will exist at prediction time? Who can explain missing or misleading records?

Google’s Rules of Machine Learning emphasizes simple baselines and sound infrastructure before unnecessary model complexity. My hiring takeaway is straightforward: do not turn an unresolved data problem into an elaborate wish list for a model specialist.

Scenario 3: You have an AI prototype but no dependable product foundation

A founder has a promising prototype, a few interested users, and no established engineering team. The immediate backlog includes authentication, billing, deployment, monitoring, and integrating a model into a coherent experience.

My first question would be whether this is primarily a founding engineer role with applied AI capability. If most of the next release is product and software infrastructure, a narrowly scoped modeling specialist may not match the work.

That does not make the AI component unimportant. It means the first hire needs the breadth to turn it into something people can use, with specialist support where needed. Describe that breadth honestly, including the ambiguity and the decisions the founder will continue to own.

If the company’s central challenge is developing a novel model or research method, reconsider this recommendation. That may call for research engineering or scientific expertise outside the two-role comparison in this article.

What should you decide before opening the search?

Write a short decision brief that your founder, technical lead, and hiring manager can all agree on. I would rather start with a clear page than a long posting that hides disagreement behind a list of tools.

Use these prompts in a working session:

1. The user and outcome: Who needs this system, and what task should become possible or meaningfully better?
2. The current obstacle: Is progress limited by application engineering, model quality, data readiness, or an unresolved product decision?
3. The starting point: What code, data, infrastructure, and evaluation already exist? What is still a prototype?
4. The first meaningful deliverable: What should the hire be able to demonstrate, and who will judge whether it is useful?
5. The ownership boundary: Which responsibilities belong to this person, and which have named partners?
6. The working reality: What support, decision authority, location expectations, and operational responsibilities come with the role?

Make the deliverable specific enough to discuss without pretending you know every implementation detail. “Own AI strategy” gives a candidate very little to work with. “Assess our support-assistant prototype and recommend a release plan with evaluation and escalation criteria” is a clearer starting assignment.

Then check whether the scope is achievable with the resources you have. If the team needs several specialties, choose the first bottleneck and sequence the others. A single job description should not become a substitute for a staffing plan.

This is the clarity I want behind an AI and ML recruiting search: a defined problem, a realistic scope, and enough honesty that the candidate can decide whether the role fits them too.

What evidence should you look for in candidates?

Look for evidence that matches the responsibility you are hiring for. A polished portfolio is useful context, but I want the conversation to distinguish individual ownership from the work of a larger team.

For an applied AI role, ask the candidate to walk through a feature from user need to release. What did they integrate? How did they decide whether the outputs were good enough? What happened when retrieval failed, the model returned an unsuitable answer, or the service became slow?

For an ML role, ask them to explain the relationship between the data, the evaluation, and the production system. How did they choose a baseline? How did they prevent information unavailable at prediction time from influencing validation? What would trigger investigation or a model update?

In both cases, have a qualified technical interviewer assess the answers. My role here is to help define the hiring evidence, not to imply that a recruiter conversation replaces technical evaluation.

Anthropic’s evaluation guidance starts with specific, measurable success criteria. That principle is useful for your hiring discussion as well: candidates should be able to explain what they measured and why those measures mattered for the task.

Keep the process focused. I recommend a structured conversation about relevant work, a bounded technical exercise or walkthrough, and a clear discussion of expectations. Do not add rounds simply because several people want a separate opportunity to ask the same questions.

Most importantly, leave room for a strong candidate to challenge your assumptions. Someone who tells you the data is not ready, the release criteria are unclear, or the role combines incompatible priorities may be showing exactly the judgment you need.

Can one person own both AI application work and ML engineering?

Yes, some engineers can cover both. The question is whether one person can own the particular workload you are proposing, with the support and time available. Breadth is a capability; capacity is a constraint.

I would separate essential responsibilities from capabilities that might become useful later. If the first deliverable uses an existing model, require the skills needed to ship and evaluate that system. Do not automatically add advanced model-training requirements because the company might eventually pursue a different architecture.

The reverse matters too. If model development is central to the business, do not assume that experience integrating a model API demonstrates the modeling and statistical judgment your project needs.

Write down who reviews security, who owns data access, and who responds to production problems. The NIST Generative AI Profile describes risk-management considerations across the AI lifecycle. It is a useful reference for planning responsibilities; it is not evidence that hiring one specialist makes a system safe or compliant.

My recommendation is to make cross-functional support explicit in the offer conversation. “You will partner with our platform lead” describes a different job from “you will establish the platform.” Both can be valid opportunities. They should not be presented as the same one.

How do you turn the decision into a realistic job description?

Once you have chosen the primary responsibility, write the posting around outcomes and ownership. Use the recognizable title that best fits, then clarify the scope in the opening paragraph.

For an applied role, start with the AI engineer job-description template and adapt it to your actual system. For an ML role, make the modeling, data, validation, and operating responsibilities equally explicit. Avoid copying application requirements into a modeling search just to make the posting sound comprehensive.

Before publishing, ask someone outside the hiring discussion to read it. Can they identify the first deliverable? Can they tell what already exists? Can they distinguish required experience from skills a good hire could learn?

Finally, plan the handoff into the company. My view on onboarding is simple: nobody is truly plug-and-play. Even an excellent engineer needs access, context, decisions, and time to understand how your team works. A thoughtful first assignment should expose unknowns early and establish a baseline, not demand that the hire validate every assumption behind the business immediately.

Choose the work first. Then find the person with the judgment, relevant evidence, and room to grow into it.

Need help defining your first AI or ML hire?

If your team agrees it needs AI capability but disagrees about the role, let’s talk about the search. Bring the product goal, the current team, and the work that is blocked. Those are more useful starting points than a list of fashionable titles.

Sources and further reading

• Google Cloud: Professional Machine Learning Engineer, an example of the breadth and overlap of ML responsibilities.
• Google: Rules of Machine Learning, engineering guidance on baselines, infrastructure, and model development.
• Microsoft Learn: Retrieval-augmented generation overview, application architecture and retrieval challenges.
• Anthropic: Define success criteria and build evaluations, guidance for evaluating language-model applications.
• NIST: Generative Artificial Intelligence Profile, a framework companion for considering generative AI risks.

Frequently Asked Questions

Is an AI engineer the same as a machine learning engineer?

The titles overlap, and employers do not use them consistently. In this hiring guide, applied AI engineering emphasizes software built around existing models, while ML engineering emphasizes models and their supporting lifecycle. Read the responsibilities before treating the titles as different candidate pools.

Should a startup hire an AI engineer or ML engineer first?

Start with the work blocking a useful product outcome. An applied AI engineer may fit an existing-model application, while an ML engineer may fit a product that depends on developing and operating predictive models. If basic product infrastructure is missing, a founding engineer with relevant AI experience may be the more appropriate first role.

Do I need an ML engineer to build a RAG application?

Not automatically: a capable applied AI engineer may be able to build it with appropriate data, platform, and security support. The role needs retrieval and evaluation judgment as well as software engineering. More specialized modeling or retrieval work may change the expertise required.

Can an experienced software engineer become an AI engineer?

A strong software engineering background can be relevant to applied AI work. Assess whether the candidate can evaluate model behavior, handle failure cases, and explain the limitations of the system they are building. Do not assume either that a missing AI title disqualifies them or that software experience alone proves readiness.

Should we require a PhD for an AI or ML engineering role?

Set degree requirements according to the work rather than the prestige of the credential. Research-intensive roles may need advanced research expertise, while an applied product role may be better assessed through relevant engineering evidence. Be clear about the capability you need and consider credible ways candidates can demonstrate it.

How do we avoid writing an unrealistic AI job description?

Name the first meaningful deliverable, the existing resources, and the responsibilities the hire will actually own. Separate immediate requirements from future possibilities and identify the people supporting data, product, security, and infrastructure. If the posting describes several full-time jobs, narrow or sequence the work before starting the search.