A machine learning engineer builds the model. An AI engineer builds the product that uses a model somebody else built.
Employers apply both titles loosely, so the work a listing describes separates the two jobs more reliably than the words in the title.
What does a machine learning engineer do?
A machine learning engineer owns the model. Training data, features, architecture, evaluation, retraining when it drifts, and the infrastructure that serves predictions to millions of people without falling over. PyTorch and TensorFlow are the daily tools. Kubernetes turns up because serving a model at that scale is an infrastructure problem rather than an API call.
The companies hiring them here were running machine learning long before the current wave. Spotify, Reddit, Twilio, Delivery Hero, Hugging Face, Synthesia, Faculty. Their job titles are narrow enough to tell you the team already exists, like “Ads Foundational Representations”, “ML Efficiency” and “Content Intelligence”. Nobody writes a title like that unless there is a system to maintain.
What does an AI engineer do?
An AI engineer owns what the model does. Retrieval, prompting, evaluation, agent orchestration, the service wrapped around the model, and the guardrails that stop it embarrassing the company in front of a customer. LangChain and RAG appear on this side of the line and almost nowhere else. Stack Overflow’s 2025 developer survey found LangChain in use by 33% of developers building agents, and 84% of all developers using or planning to use AI tools.
The employers look different too. AI engineer roles here come from consultancies and product teams. Devoteam, Multiverse, Databricks, n8n, Writer, Channable, Netguru. These are companies fitting AI to something that already exists, which is why AWS, Snowflake and Azure keep appearing beside LangChain.
AI engineer vs machine learning engineer at a glance
| Machine learning engineer | AI engineer | |
|---|---|---|
| What you own | The model | The product around the model |
| Core tools | PyTorch, TensorFlow, Kubernetes | LLM APIs, RAG, LangChain, agents |
| Typical employer | Companies already running ML at scale | Consultancies and product teams adding AI |
| Live roles on this board | 15 | 28 |
| Senior or above | 8 of 15 | 14 of 28 |
| Usual way in | You have already trained models | You have shipped something with an LLM |
Is AI engineering or machine learning in higher demand in Europe?
AI engineering, and the gap is not close.
Of the 678 AI roles open across Europe on this board, 513 describe the work concretely enough to sort. 478 of them want somebody to build with a model. 124 want somebody to build the model. We sampled every live listing and counted a skill when the employer names it in the description.
LinkedIn’s Jobs on the Rise 2026 ranks AI Engineer the fastest-growing job title in the UK, France, Italy, the Netherlands and Spain, and second in Germany behind Head of AI. Machine learning engineer appears on none of those lists.
Agents are named in 332 listings, more than half the board. PyTorch, the tool you reach for when you are training something, appears in 52. TensorFlow in 26. Fine-tuning in 31.
Do the two job titles mean the same thing?
No, but plenty of employers write them as though they do. Some roles advertised as AI engineering are backend jobs with a model bolted on. Some machine learning engineer roles at smaller companies are AI engineering in practice, because there is no training pipeline to own yet.
Read the tools instead of the title. A listing naming PyTorch and evaluation wants somebody who can train. A listing naming RAG and agents wants somebody who can ship. When a listing names both, it usually means a small team where one person does everything.
Can you get one of these jobs without being senior?
Not easily, and machine learning is the harder door.
Of the 15 machine learning engineer roles open here, 8 are senior, staff, principal or lead. On the AI engineer side it is 14 of 28. Across all 678 roles on the board there are 10 internships and 2 junior positions.
That is not a quirk of this board. Indeed’s Hiring Lab found that 71% of the growth in software development postings between May 2025 and May 2026 came from senior roles, with 37% coming from jobs that name AI in the title. Companies hire juniors where somebody can sit next to them, and model training is the hardest work to supervise over Slack.
A small number of employers also account for most of the openings, so the market is narrower than a headline count suggests. Indeed found that almost 90% of AI-related job postings in 2025 sat with just 1% of hiring firms.
Should you become an AI engineer or a machine learning engineer?
Become a machine learning engineer if you have trained models with your own hands rather than listed them on a CV, and you are already senior. The roles are fewer, they concentrate in a handful of large product companies, and the interview will go after fundamentals.
Become an AI engineer if you are a strong software engineer who has built something with an LLM. You do not need to have trained a model. You need to have shipped one into production and be able to explain what broke and what you did about it. It is also the more realistic entry point, because it is where the roles below senior still exist.
If your background is backend engineering, you are closer to the second one than you think. Half these listings are backend jobs with AI attached.
Start with remote AI engineer jobs and remote machine learning jobs. If model building appeals, MLOps and AI infrastructure sits beside it. If the product side does, look at LLM and generative AI roles.