A Data Engineer makes a company’s data usable. A Data Scientist works out what that data says. An AI Engineer builds applications that act on it. The three roles depend on each other in roughly that order, which is why the right first hire is usually the one whose work is currently missing.
For many mid-market companies, that first hire is a Data Engineer. But it depends on the state of your data, the problem you want to solve and what your existing team can already do. This guide compares the three roles, adds the Machine Learning Engineer for context, covers what each one earns and gives a practical way to decide who to hire first.
At Search Services, our AI staffing division recruits all four roles for employers in Houston and nationwide, so the comparisons below reflect how companies are actually using these titles.
AI Engineer vs Data Scientist vs Data Engineer at a Glance
| Role | Main job | Typical output | Hire first when |
|---|---|---|---|
| Data Engineer | Collects, cleans and moves data so other people and systems can use it | Pipelines, data warehouses, integrations, reliable datasets | Your data is scattered across systems, hard to access or not trusted |
| Data Scientist | Analyzes data to answer business questions and build predictive models | Forecasts, experiments, statistical models, recommendations | Your data is in reasonable shape and you need better decisions from it |
| AI Engineer | Builds applications on top of AI models, often large language models (LLMs) | Assistants, automations, AI features inside products and workflows | You have a clear use case, usable data and need something built |
| Machine Learning Engineer | Trains, deploys and maintains Machine Learning models in production | Model services, training pipelines, monitoring | You need custom models that run reliably at scale |
What Does a Data Engineer Do?
A Data Engineer builds and maintains the systems that collect, store and deliver data. Most companies hold their information in many places: an Enterprise Resource Planning (ERP) system, a Customer Relationship Management (CRM) platform, spreadsheets, operational databases and documents. The Data Engineer connects those sources and turns them into something consistent.
Typical responsibilities include:
- Building data pipelines that move and transform data
- Designing and maintaining data warehouses or data lakes
- Integrating data from business systems and outside sources
- Monitoring data quality and fixing what breaks
- Managing access, security and cost on cloud data platforms
Without this work, Data Scientists and AI Engineers spend much of their time finding and cleaning data instead of doing the job they were hired for.
What Does a Data Scientist Do?
A Data Scientist uses statistics, programming and business knowledge to find patterns in data and turn them into decisions. Where a Data Engineer asks whether the data is available and correct, a Data Scientist asks what it means and what is likely to happen next.
Typical responsibilities include:
- Framing a business question so it can be answered with data
- Exploring and analyzing datasets
- Building predictive and statistical models
- Designing experiments and measuring results
- Explaining findings to people who are not technical
Data Scientists are most valuable where a company needs forecasting, pricing, risk scoring, optimization or customer analytics. Many of those problems do not require generative AI at all.
What Does an AI Engineer Do?
An AI Engineer builds software that uses AI models to do useful work. In most companies today that means applying existing models, especially large language models, rather than inventing new ones. The job is closer to software engineering than to research.
Typical responsibilities include:
- Building applications and assistants on commercial or open-source models
- Connecting models to company data through retrieval and integrations
- Designing prompts, workflows and agents
- Testing and evaluating output quality
- Deploying, monitoring and controlling the cost of AI features
A strong AI Engineer cares about what happens after the demonstration: accuracy, failure handling, security and whether people actually use the result.
Where Does a Machine Learning Engineer Fit?
The titles AI Engineer and Machine Learning (ML) Engineer overlap, and some companies use them interchangeably. Where they differ, the ML Engineer works closer to the model itself: preparing training data, training and tuning models, and running them in production. The AI Engineer more often assembles existing models into products.
If your plan is to apply proven models to your own workflows, you probably need an AI Engineer. If you need custom models trained on your own data and served at scale, you need ML engineering.
Data Engineer vs Data Scientist: What Is the Difference?
The simplest way to separate them: a Data Engineer builds the supply of data, and a Data Scientist uses it. The Data Engineer is measured on whether data arrives complete, on time and in a usable form. The Data Scientist is measured on whether the analysis or model improves a decision.
The skills overlap in programming and databases, but the daily work is different. Data Engineers spend their time on infrastructure, pipelines and reliability. Data Scientists spend theirs on analysis, modeling and communication. Hiring a Data Scientist before the data is usable is one of the most common and expensive sequencing mistakes.
AI Engineer vs Data Scientist vs Data Engineer Salary
Pay varies widely by source, seniority and location, so treat any single figure as a starting point. The table below uses one source for all four roles so the comparison is consistent.
| Role | Average base salary | Average total compensation |
|---|---|---|
| AI Engineer | $184,757 | $211,243 |
| Machine Learning Engineer | $162,080 | $212,022 |
| Data Scientist | $128,067 | $145,852 |
| Data Engineer | $125,983 | $150,234 |
Source: Built In salary data for AI Engineers, Machine Learning Engineers, Data Scientists and Data Engineers in the United States, accessed October 2026.
A few points help put those numbers in context:
- The U.S. Bureau of Labor Statistics (BLS) reports a median annual wage of $120,230 for data scientists in May 2025 and projects employment to grow 35% from 2025 to 2035.
- The BLS does not track Data Engineers separately. The closest category, database architects, had a median annual wage of $139,500 in May 2025.
- There is no federal category for AI Engineers yet, and private sources disagree. Glassdoor, for example, puts median total pay for AI Engineers at about $145,000, well below the Built In average.
- Engineers with production experience in generative AI are scarce, which pushes offers for proven candidates toward the top of any range.
Which Role Should Your Business Hire First?
Hire against the constraint that is holding the work back. Four questions usually settle it.
Is your data scattered, hard to reach or unreliable?
Hire a Data Engineer first. No model or analysis will be better than the data underneath it.
Is your data usable, but you are not getting answers from it?
Hire a Data Scientist. This is the right first hire when the goal is better forecasting, measurement or decision support.
Do you have a defined use case and usable data, and need something built?
Hire an AI Engineer. Internal knowledge assistants, document processing and workflow automation typically fall here.
Do you have prototypes that work in testing but not in production?
Hire a Machine Learning Engineer, or an AI Engineer with strong deployment experience, to make the system reliable.
These three roles are only part of a complete team. Someone also has to own the business problem and the governance around it. Our guide to the first five hires on an AI team covers that wider sequence.
Can One Person Cover All Three Roles?
Sometimes, for a while. Early in an AI program, a strong generalist can build a pipeline, run an analysis and stand up a first application. That works for a pilot.
It rarely holds once the work reaches production. The roles pull in different directions, and people who are excellent at all three are among the hardest professionals to find and keep. A more realistic plan is to hire for the current constraint and add the next role when the work demands it.
How Search Services Helps Companies Hire AI and Data Talent
Search Services has recruited in Houston for more than 25 years. Through Search AI and our Search Technology recruiting division, we help employers define the role before the search starts, then find people who have done the work.
In May 2026, Search AI announced its 24th AI or AI-adjacent placement, including a Principal Data Engineer, a Principal Data Scientist, an Azure Data Architect and an AI Engineer. That range mirrors what this article describes: companies building AI capability need data, analytics and engineering skills together.
Houston employers hiring across the wider technology function can also work with our Houston IT recruiters. To talk through which role your business should hire first, contact Search Services.
FAQs About AI Engineers, Data Scientists and Data Engineers
What is the difference between a data engineer and a data scientist?
A Data Engineer builds and maintains the pipelines and platforms that make data available and reliable. A Data Scientist analyzes that data and builds models to answer business questions. One supplies the data; the other uses it.
Is an AI engineer the same as a machine learning engineer?
Not always. Many companies use the titles interchangeably. Where they differ, an AI Engineer builds applications on existing models, while a Machine Learning Engineer trains, deploys and maintains the models themselves.
Who earns more: an AI engineer, a data scientist or a data engineer?
In Built In’s U.S. salary data, AI Engineers have the highest average base salary of the three at $184,757, compared with $128,067 for Data Scientists and $125,983 for Data Engineers. Other sources report lower AI Engineer figures, so use ranges rather than a single number when budgeting.
Should a company hire a data engineer or a data scientist first?
If the data is fragmented or unreliable, hire the Data Engineer first. If the data is already accessible and trusted, a Data Scientist can deliver value immediately.
Is there an entry-level AI engineer role?
Yes, but it is less common than entry-level roles in software or data. Many AI Engineers move into the role after several years as software engineers, data engineers or data scientists, which is one reason experienced candidates are hard to find.
Can a software engineer become an AI engineer?
Often, yes. Building AI applications draws heavily on software engineering skills. A capable engineer who learns model evaluation, retrieval and deployment can be a faster route to an AI Engineer than an outside search, and it is worth reviewing your existing team before you hire.
Sources
- U.S. Bureau of Labor Statistics: Data Scientists, Occupational Outlook Handbook
- U.S. Bureau of Labor Statistics: Database Administrators and Architects, Occupational Outlook Handbook
- Built In: AI Engineer salary in the U.S.
- Built In: Machine Learning Engineer salary in the U.S.
- Built In: Data Scientist salary in the U.S.
- Built In: Data Engineer salary in the U.S.
- Glassdoor: AI Engineer salaries
- Search Services: How to Build an AI Team, the First 5 Hires
- Search Services: Search AI Division Marks 24th AI Placement
