AI engineering jobs in Europe are growing fast: PredictLeads job-openings data recorded 6,633 AI-engineering openings across Europe in July 2026, up 57.1% from 4,221 in July 2025. That is the clearest signal in our dataset, because July is the only month that lines up cleanly for a year-on-year read in our reliable window. Across the full window we track (18 June 2025 through August 2026), we counted 72,533 AI-engineering openings in Europe, with 31,718 of them in the last six months alone. This report breaks that down by country, role, skill, published salary, and hiring source, and it closes with exactly how we measured it and where the numbers stop short.
TLDR:
- AI engineering hiring in Europe grew 57.1% year on year in July (4,221 in July 2025 to 6,633 in July 2026), the only fully comparable month in our reliable window.
- The United Kingdom and Germany together account for roughly 40% of all European AI-engineering openings, and the UK, the single largest source, is not in the EU.
- Python leads the skill list, named in 46.6% of postings, with cloud platforms (Azure 19.8%, AWS 19.4%) and DevOps tooling close behind.
- Only about 14.2% of postings publish any salary; where an annual figure is stated, the median is about $74,700, but the sample is UK/GBP-skewed, so read it as “what employers publish,” not the market rate.
- These figures come straight from the PredictLeads Job Openings dataset, which holds 279.2M+ job records since 2018 across 2.9M+ companies, each categorized with O*NET occupation codes.
What “AI Engineering Jobs in Europe” Means in This Report
An AI engineering role, in this dataset, is a posting whose normalized job title names an AI or machine-learning concept (machine learning, artificial intelligence, deep learning, computer vision, NLP, LLM, generative AI, MLOps, neural networks, or reinforcement learning) combined with an engineering or technical function (engineer, developer, scientist, architect, researcher, specialist, or lead), plus standalone titles like “AI/ML Engineer.” A generic “Data Scientist” title with no AI or ML wording is largely excluded, which keeps the count focused on roles that build AI systems rather than every analytics job.
“Europe” here is geographic Europe, not the European Union. It includes the 27 EU member states plus non-EU countries such as the United Kingdom, Switzerland, Norway, Ukraine, and the Balkans. That distinction matters throughout this report, because the UK leads every ranking below and sits outside the EU.
AI Engineering Hiring in Europe Is Up 57.1% Year on Year
The headline is growth. In July 2025, PredictLeads recorded 4,221 AI-engineering openings across Europe. In July 2026, that figure reached 6,633, a 57.1% year-on-year increase. We lead with this comparison deliberately: July is the only month that lines up cleanly on both sides of our reliable window, so it is the single most defensible growth read we can offer. The two July bars are highlighted in amber below.
Month-on-month movement tells a noisier story, and we would caution against reading too much into any single swing. Across the last six months the path runs 7,943, then 4,281 (down 46.1%), 5,250 (up 22.6%), 4,484 (down 14.6%), 6,633 (up 47.9%), and 3,127 (down 52.9%, partial). Those percentages look dramatic, but they are heavily shaped by when our crawlers indexed each site, which is why the year-on-year July figure is the number to anchor on. We also do not compare August to August: both years are partial (3,803 versus 3,127), so that pairing is not valid. The PredictLeads Job Openings dataset timestamps every record with first_seen_at, which is what makes a like-for-like month comparison possible in the first place.
Where the Jobs Are: Country Ranking
The United Kingdom and Germany dominate. Together they account for roughly 40% of all European AI-engineering openings in the last six months, and the UK, the single largest source, sits outside the EU. France, Spain, and Poland form a clear second tier, and Southern and Nordic markets fill out the rest.
Poland at fifth and Portugal at eighth are worth noting: both rank ahead of larger economies, consistent with the growth of engineering hubs in those markets. Every one of these counts is derived from the location_data field on each job record, which resolves city, state, country, and continent.
The Role Mix: Most Openings Are Straightforward “ML/AI Engineer” Titles
Nearly two-thirds of AI-engineering openings use a direct “ML/AI Engineer” title. Specialized tracks like NLP/LLM/GenAI, MLOps, and computer vision are still a small share of the total, which suggests most European employers are hiring generalist AI engineers rather than narrow specialists.
These groupings are derived from normalized titles, not a single labeled “role type” field. The Job Openings dataset also carries normalized_title, seniority, and O*NET onet_data, which is what allows this kind of consistent cross-company grouping.
Most-Requested Skills: Python, Then Cloud and DevOps
Python is the common denominator of European AI engineering, named in 46.6% of postings. After Python, the list is dominated by cloud platforms and DevOps tooling rather than modeling frameworks, which tells you employers expect AI engineers to ship and operate systems, not only train models.
Below the top 15, the long tail includes TypeScript and Git (6.8% and 6.7%), NumPy (6.3%), LangGraph (5.6%), scikit-learn (4.7%), Terraform (4.6%), Databricks (4.2%), Claude (4.2%), MLflow (3.7%), and Claude Code (3.6%). The mix of orchestration frameworks (LangChain, LangGraph) alongside classic ML libraries (PyTorch, TensorFlow, scikit-learn) shows LLM application work now sits next to traditional model building in the same job descriptions. These skill mentions are read from job descriptions, the same signal PredictLeads uses for seen_on_job_openings technology evidence.
The Fastest-Growing Skills Point to the Microsoft and Azure AI Stack
Comparing mention rates in the first half of the window (March to May, n=17,474) against the second half (June to August, n=14,244), the fastest-rising skills cluster around generative-AI tooling and the Microsoft/Azure AI stack.
These are growth rates off small bases in several cases, so treat them as directional. Non-skill terms such as an applicant-tracking system, “Advertising,” and “Social Media” were excluded from this ranking. The theme is consistent: generative-AI and LLM tooling, and the Microsoft/Azure AI ecosystem in particular, are the fastest-climbing requirements in European AI-engineering postings.
Salary: What Employers Publish, Not the Market Rate
Most European AI-engineering postings do not state pay. Only about 14.2% of postings (4,498 of 31,718) include any salary at all, and 3,446 give an annual figure. Where an annual salary is published, the distribution runs from about $50,700 at the 10th percentile to about $159,000 at the 90th, with a median near $74,700.
Read the median as “what employers publish,” not the true market average. The published-annual sample is skewed toward the UK: of the annual figures, 1,851 are in GBP, 1,259 in EUR, 289 in USD, and 42 in CHF. Because British listings publish salary more often, the median leans British and does not represent a pan-European rate. The Job Openings dataset stores this as salary_data, with low, high, currency, a normalized USD value, and a time unit, which is what lets us split annual figures cleanly from other pay periods.
Remote Share: Why We Are Not Publishing a Number
We are not reporting a remote-work percentage, because it is not reliably measurable in this dataset. Remote status is not a structured field; it usually lives in the job title or description text and is stripped during normalization. Rather than publish a figure we cannot stand behind, we are leaving this metric out. This is the kind of honest gap worth stating plainly, and it is a good example of why PredictLeads timestamps and structures the fields it does capture, so downstream users know exactly what a number rests on.
Top Hiring Sources, and Why the List Is Not a Ranking of Employers
This ranking is by the site where the role was found, not by verified end employer. That means the largest volumes include AI training-data platforms and reposting job boards, not only companies hiring directly. We flag those clearly below so the list is not misread.
| Source | Openings | Type |
|---|---|---|
| Mindrift | 1,430 | AI data platform (not an end employer) |
| GCHQ (all domains) | 1,093 | Employer (UK government) |
| Simone Quartucci | 516 | Aggregator / reposter |
| VermarkterCheck | 304 | Job board (DE) |
| xpat rentals | 219 | Aggregator |
| 3BMeteo | 210 | Employer (IT) |
| Ampin | 197 | Employer / platform |
| BIP Group | 175 | Employer (consulting) |
| Resu Flex | 165 | Platform |
| Alignerr | 159 | AI data platform (not an end employer) |
| New Look | 156 | Employer (retail) |
| szukamy | 154 | Job board (PL) |
| IT Boltwise | 152 | News / board (DE) |
| SAP | 72 | Employer (software) |
| Tether | 68 | Employer (fintech) |
| GlobalLogic | 67 | Employer (IT services) |
| Thales | 65 | Employer (defense / tech) |
| T-Systems | 62 | Employer (IT services) |
Among the recognizable end employers, the standouts are GCHQ (UK government), SAP, Tether, Thales, GlobalLogic, T-Systems, New Look, and BIP Group, a spread that runs from government and defense through enterprise software, IT services, fintech, and retail. The non-employer sources near the top (Mindrift and Alignerr are AI data platforms; Simone Quartucci and xpat rentals are aggregators; VermarkterCheck, szukamy, and IT Boltwise are job boards) are exactly why we do not present this as a clean employer leaderboard. Each source is tracked through the URL where the posting was found, which is how PredictLeads preserves source transparency on every record.
How We Measured This
This report is built entirely from the PredictLeads Job Openings dataset, counted by the month each posting was first seen. A few things you should know before quoting the numbers:
- Staging snapshot. These figures come from a staging snapshot that can run up to about a week behind production, so they are not a live feed.
- Geographic Europe, not the EU. “Europe” means the EU-27 plus non-EU countries including the UK, Switzerland, Norway, and Ukraine. The UK leads and is not in the EU.
- Reliable window starts 18 June 2025. Index coverage is reliable only from that date, so a full six-month year-on-year comparison is not possible; July is the only month that lines up on both sides.
- August 2026 is partial. The August count is incomplete, so we exclude it from year-on-year reads and flag it in the trend chart.
- Classification is derived, not labeled. Role and skill groupings are inferred from normalized job titles and detected technologies in the description text, not from a pre-labeled field, and remote status is not reliably captured at all.
If you want to reproduce this kind of analysis, the underlying fields (normalized_title, location_data, salary_data, onet_data, first_seen_at, and last_seen_at) are documented in the PredictLeads API documentation. Occupation coding follows the industry-standard O*NET taxonomy, and for broader context on AI’s trajectory, the Stanford HAI AI Index tracks the field at large.
How PredictLeads Tracks Hiring Signals Like These
Everything above comes out of one dataset. The PredictLeads Job Openings dataset holds 279.2M+ historical job records since 2018 across 2.9M+ company websites, with 10.2M active openings at any time, each categorized with O*NET occupation codes and timestamped with first_seen_at and last_seen_at. Those timestamps are what make trendlines, year-on-year comparisons, and hiring velocity possible in the first place.
Hiring data gets sharper when you combine it with adjacent signals. The same job descriptions that name skills also feed Technology Detections, where a required tool becomes evidence of what a company uses via the seen_on_job_openings source (part of a 1.5B+ detection dataset across 50,000+ technologies). Pair hiring velocity with Financing Events to see which funded companies are staffing up, or with Similar Companies to build a lookalike set of employers hiring for the same roles.
PredictLeads delivers all of this through four methods: API, flat files, webhooks, and MCP. The data is built from public sources only, and the company maintains SOC 2 Type II, GDPR, and CCPA compliance.
Final Thoughts on AI Engineering Jobs in Europe
The direction is clear even where the month-to-month noise is not: AI engineering hiring in Europe grew 57.1% year on year in July 2026, the UK and Germany drive roughly 40% of it, Python and cloud skills define the role, and generative-AI tooling is the fastest-rising requirement. The salary and source figures come with real caveats, which is exactly why we state them. Track any of these trends yourself against live data below.
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Frequently Asked Questions
How fast are AI engineering jobs growing in Europe in 2026?
AI engineering openings in Europe grew 57.1% year on year in July, from 4,221 in July 2025 to 6,633 in July 2026, according to PredictLeads job-openings data. July is the only month that lines up cleanly for a year-on-year read in our reliable window, which starts 18 June 2025. Month-on-month figures are noisier because crawl timing shifts when postings are indexed. The Job Openings dataset timestamps every record with first_seen_at, which is what makes the comparison possible.
Which European countries have the most AI engineering jobs?
The United Kingdom leads with 6,501 openings over the last six months, followed by Germany at 5,524, and together the two account for roughly 40% of the European total. France (2,894), Spain (2,426), and Poland (2,116) form the next tier. Note that “Europe” here is geographic, so the UK counts even though it is not in the EU. These counts come from the location_data field on each PredictLeads job record.
What skills do European AI engineering jobs ask for most?
Python is the most requested skill, named in 46.6% of AI-engineering openings, followed by Azure (19.8%), AWS (19.4%), CI/CD (18.1%), and Docker (13.7%). Cloud and DevOps tooling ranking so high suggests employers expect AI engineers to deploy and operate systems, not only train models. These skill mentions are read from job description text, the same signal PredictLeads uses for its seen_on_job_openings technology evidence.
What is the median salary for AI engineering jobs in Europe?
Where an annual salary is published, the median is about $74,700, but only around 14.2% of postings state any salary at all. Read this as “what employers publish,” not the true market rate, because the published-annual sample is skewed toward UK listings in GBP. PredictLeads stores this in the salary_data field with low, high, currency, a normalized USD value, and a time unit, which lets us isolate annual figures cleanly.
Where does this AI engineering hiring data come from?
It comes entirely from the PredictLeads Job Openings dataset, a staging snapshot counted by the month each posting was first seen. The dataset holds 279.2M+ job records since 2018 across 2.9M+ companies, each categorized with O*NET occupation codes and timestamped. Role and skill groupings are derived from normalized titles and detected technologies rather than pre-labeled fields, and figures are reliable only from 18 June 2025 onward.