AI engineer vs ML engineer vs LLM engineer vs data scientist: who should you hire?
By Tarannum Khan|Updated
Short answer
Hire an AI or LLM engineer to build product features on existing models, such as assistants that answer from your documents and agents that take actions. Hire a machine learning engineer to train, ship and run your own models. Hire a data scientist to analyse data and answer business questions. The titles overlap, so judge people by what they have shipped.
Key takeaways
- AI and LLM engineers build software on existing models: retrieval, agents, evaluation, integration and operations.
- Machine learning engineers train, package and monitor models, including forecasting, recommendations and detection.
- Data scientists analyse data and build models to answer business questions.
- BLS has no AI engineer category. It put the median US data scientist wage at $120,230 in May 2025.
- Hire for the output you need, insight, a model or a feature, and test with real work.
What is the difference between these AI roles?
The difference is the output. A data scientist produces insight and analysis. A machine learning engineer produces a trained model that runs reliably. An AI or LLM engineer produces a product feature built on a model, such as an assistant or an agent. Many people cover more than one of these.
Swipe the table sideways to see every column.
| Role | Main output | Typical work | Hire when |
|---|---|---|---|
| AI engineer | Product features built on models | Retrieval, agents, evaluation, integration and operations | AI is part of your product or a core workflow |
| LLM engineer | Features built on large language models | Assistants, document extraction, search that answers, agents | Your feature runs on a language model |
| Machine learning engineer | Trained models running in production | Data pipelines, training, packaging, monitoring, retraining | You need your own model, such as a forecast or a classifier |
| Data scientist | Analysis and models that answer questions | Exploring data, testing ideas, statistical models | You need to know what the data says before building anything |
Titles vary between companies. An "AI engineer" at one company may do machine learning engineering at another.
What does an AI or LLM engineer do?
An AI engineer builds and runs software that uses models, now mostly large language models. Engineers who focus on language-model products are often called LLM engineers. The job is mostly engineering around the model: data, prompts, tool calls, testing, cost and monitoring.
- Retrieval: RAG systems that answer from your own documents and cite them.
- Agents: AI agents that use tools such as a CRM, within set limits.
- Evaluation: test sets and measures that show whether a change made answers better or worse.
- Integration: connecting models to your product through APIs, queues and databases.
- Operations: watching cost, speed, errors and misuse once real users arrive.
What does a machine learning engineer do?
A machine learning engineer turns models into reliable software. They build data pipelines, train and evaluate models, package them behind APIs, and monitor them once they are live. Machine learning covers more than language models: forecasting, recommendations, fraud detection, image recognition and classification.
Hire one when you need a model trained on your own data, not only a feature built on someone else's. Our machine learning engineers page lists the typical work.
What does a data scientist do?
A data scientist mainly analyses data and builds models to answer business questions (BLS). The output is usually insight: what is happening, why, and what might work. BLS put the median US data scientist wage at $120,230 in May 2025.
BLS has no separate category for AI engineers. For comparison, it put the median wage for software developers at $135,980 in May 2025 (BLS).
Which role should you hire?
Start from the problem, not the title. If you want a feature that answers or acts, hire an AI or LLM engineer. If you need a model trained on your own data, hire a machine learning engineer. If you first need to understand your data, hire a data scientist.
| Your problem | Hire |
|---|---|
| Customers or staff need answers from your documents | AI / LLM engineer |
| A task across several systems should run itself, within limits | AI / LLM engineer, ideally one who has built agents |
| You need a demand forecast, recommendations or fraud detection | Machine learning engineer |
| A model works in a notebook but not in production | Machine learning engineer |
| You don't yet know what your data can tell you | Data scientist |
| You want to try a prototype before committing | A strong full-stack developer can often build one |
How do you test candidates for an AI role?
Ask what they have shipped to real users and how they measured it. Strong candidates can show a test set, explain a failure and say how they fixed it, and know the security risks of AI features. Then give them a short task on your real problem.
- Shipped work: something real users relied on, not only demos or notebooks.
- Measurement: how they knew the answers or predictions were good enough.
- Simplicity: whether they try retrieval, prompts or a simpler tool before training a model.
- Security: whether they know risks such as prompt injection and excessive permissions (OWASP).
- Code review: whether they test and review AI-generated code before it ships.
Where does Webify.AI fit?
We supply AI / LLM engineers and machine learning engineers, so we have an interest in this guide. Both are billed at our AI engineer rates: $45 an hour, or $60 for a senior AI engineer who leads the design.
- For software around a model without AI work, a senior developer costs $35 an hour.
- Tell us your models and tools, and we put forward engineers whose experience matches them. If we don't have a match, we'll say so.
- You interview the named engineers, and start with a two-week trial on your real work.
- We hold no ISO 27001 or SOC 2 certification. Our security page explains what we do instead.
Frequently asked questions
Is an AI engineer the same as a machine learning engineer?
Not quite. A machine learning engineer trains and runs models. An AI engineer mainly builds product features on existing models. Many people do both, so ask what they have shipped.
What is an LLM engineer?
An AI engineer who focuses on products built on large language models, such as assistants, document extraction, search that answers questions, and agents.
Do I need a data scientist to build an AI chatbot?
Usually not. A chatbot that answers from your documents is mostly engineering work: retrieval, prompts, testing and integration. That is an AI or LLM engineer's job.
What does a data scientist earn in the US?
BLS put the median US data scientist wage at $120,230 in May 2025. BLS has no AI engineer category; the median for software developers was $135,980.
Should my first AI hire be an engineer or a data scientist?
If you know the feature you want, an AI engineer. If you first need to find out what your data can support, a data scientist. A short paid trial with either tells you a lot.
Sources
Checked on 30 October 2026. Webify.AI figures are our published prices.
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