Top 10 Skills in Artificial Intelligence According to LinkedIn & Indeed Job Postings
Artificial intelligence is no longer a niche field reserved for research labs or tech giants—it has become a cornerstone of business strategy across the UK. From finance and healthcare to manufacturing and retail, employers are rapidly expanding their AI teams and competing for talent.
But here’s the challenge: AI is evolving so quickly that the skills in demand today may look different from those of just a few years ago. Whether you’re a graduate looking to enter the industry, a mid-career professional pivoting into AI, or an experienced engineer wanting to stay ahead, it’s essential to know what employers are actually asking for in their job ads.
That’s where platforms like LinkedIn and Indeed provide valuable insight. By analysing thousands of job postings across the UK, they reveal the most frequently requested skills and emerging trends. This article distils those findings into the Top 10 AI skills employers are prioritising in 2025—and shows you how to present them effectively on your CV, in interviews, and in your portfolio.
Quick Summary
Python programming remains the baseline for AI roles.
Core machine learning and deep learning frameworks (PyTorch, TensorFlow) are consistently in demand.
SQL & data modelling underpin trustworthy AI solutions.
Employers expect cloud expertise (AWS, Azure, GCP) and strong MLOps/deployment skills.
Demand for large language models (LLMs), prompt engineering, and RAG is rising rapidly.
NLP and computer vision remain vital across industries.
Communication & storytelling are still deal-breakers in many UK job ads.
If you’re pressed for time, mastering these ten areas will give you the strongest alignment with current AI hiring trends.
1) Python Programming (incl. Data & ML Ecosystem)
Why it’s everywhere: Python is the lingua franca for AI due to its rich ecosystem—NumPy, Pandas, scikit-learn, PyTorch, TensorFlow, Hugging Face Transformers, FastAPI for serving, Plotly/Matplotlib for insights. Across LinkedIn & Indeed ads, Python appears as a baseline requirement for AI Engineers, Data Scientists & MLEs.
What job ads often say: “Strong Python skills”, “experience with scientific Python stack”, “production-grade Python services/APIs”.
How to evidence it on your CV:
“Reduced model training time 38% by vectorising Pandas pipelines & caching feature stores.”
“Raised on-site conversion +2.1% by shipping a Python FastAPI inference service.”
Interview readiness: Be ready to code live data-wrangling and an ML pipeline skeleton in idiomatic Python.
2) Core Machine Learning (Problem Framing, Feature Engineering, Evaluation)
Why it matters: Employers want practitioners who can frame problems, design features, select algorithms, and evaluate robustly (precision/recall, ROC-AUC, PR-AUC, calibration, uplift, cost curves).
What job ads often say: “Experience building & evaluating models end-to-end”, “strong grasp of bias/variance, cross-validation, regularisation”.
How to evidence it:
Summarise a full lifecycle: problem → features → model → metrics → business impact.
Add collaboration signals: worked with product, data, compliance, & ops to deploy ethically & safely.
3) Deep Learning Frameworks: PyTorch & TensorFlow
Why it’s hot: From vision to speech to text, many AI ads target competency with at least one deep learning framework—preferably PyTorch. Familiarity with distributed training, mixed precision, and fine-tuning methods gives you a competitive edge.
What job ads often say: “Hands-on PyTorch”, “TensorFlow/Keras”, “experience with CUDA is a plus”.
How to evidence it:
Portfolio projects with reproducible training scripts, clear README, and comparison tables for baselines vs. SOTA-inspired models.
“Improved F1 from 0.71 → 0.79 by adopting focal loss & augmentations.”
4) SQL & Data Modelling
Why it matters: Whether you’re training LLMs or conventional models, you’ll spend a lot of time querying, shaping, & validating data. UK job ads commonly ask for SQL alongside Python because it underpins data quality & trustworthy features.
What job ads often say: “Strong SQL”, “ETL/ELT experience”, “dimensional modelling” or “data warehousing concepts”.
How to evidence it:
Showcase complex queries (CTEs, window functions), plus performance improvements.
Reference analytics engineering patterns (e.g., dbt tests, documented sources, lineage).
5) Cloud for AI (AWS, Azure, GCP)
Why it’s essential: Models are trained, stored & served in the cloud. Many UK ads specify a primary cloud (often AWS or Azure) but value multi-cloud awareness.
What job ads often say: “Production experience on AWS/Azure/GCP”, “infrastructure-as-code”, “cost-optimised training/inference”.
How to evidence it:
“Cut monthly inference costs 28% by right-sizing instances & moving to serverless endpoints.”
“Migrated on-prem training to SageMaker, reducing time to train from 18h → 6h.”
6) MLOps & Deployment (CI/CD, Docker, Kubernetes, Model Registry)
Why it’s rising: Employers value people who can ship reliably—containerise models, set up CI/CD, manage features & model registries, monitor drift & performance, and enforce governance.
What job ads often say: “Experience operationalising ML”, “Docker & Kubernetes”, “feature stores”, “monitoring & alerting”.
How to evidence it:
“Implemented model registry with automated canary releases & rollback; cut deployment incidents 70%.”
Add diagrams in your portfolio showing training, packaging, deployment, & monitoring loops.
7) Large Language Models (LLMs), Prompt Engineering & RAG
Why it’s exploding: UK postings increasingly reference LLMs, prompt engineering, retrieval-augmented generation (RAG), vector databases, guardrails, and evaluation. Mentions of GenAI in postings are growing rapidly.
What job ads often say: “Experience fine-tuning or instruct-tuning LLMs”, “RAG pipelines”, “prompt optimisation”, “safety filters”.
How to evidence it:
Ship a small RAG demo with evaluation: context recall@k, faithfulness scores, hallucination checks.
Document data governance: PII handling, prompt red-teaming, safe completions.
Interview readiness: Expect questions about when to RAG vs. fine-tune, latency & cost trade-offs, and evaluation approaches.
8) Natural Language Processing (beyond LLMs)
Why it’s still core: Classical NLP remains valuable: tokenisation, embeddings, entity & relation extraction, topic modelling, document classification, text normalisation, multilingual handling, and speech-to-text. Many UK sectors still need robust NLP beyond chatbots.
What job ads often say: “Experience with text analytics”, “NER”, “document processing at scale”.
How to evidence it:
“Reduced claims processing time 22% via transformer-based NER + heuristic post-processing.”
“Scaled batch inference across 40M documents using Spark NLP; throughput ↑ 4×.”
9) Computer Vision (Detection, Segmentation, OCR)
Why it endures: Retail, logistics, healthcare imaging & manufacturing use CV for quality control, shelf analytics, OCR, safety, & defect detection. Ads often seek experience with CNNs, Vision Transformers, OpenCV, ONNX, TensorRT, & edge deployment.
What job ads often say: “Real-time inference”, “camera pipelines”, “video analytics”, “OCR”.
How to evidence it:
“Achieved 94% mAP on small-object detection; reduced latency from 130ms → 48ms with INT8 quantisation.”
“Cut false positives 31% via active learning & targeted relabelling.”
10) Communication & Stakeholder Storytelling
Why it gets you hired: Employers repeatedly flag communication in postings—translating model behaviour into commercial outcomes, aligning with risk, legal & compliance, and shaping roadmaps with product & engineering.
What job ads often say: “Stakeholder engagement”, “present complex insights clearly”, “cross-functional collaboration”.
How to evidence it:
“Presented model trade-offs to execs; secured approval for staged rollout that added £1.2m ARR.”
Share dashboards, decision memos, or simplified explanations in your portfolio.
Honorable Mentions
Responsible AI, Governance & Risk
Experimentation & Causal Inference
Security & Privacy for AI
How to Prove These Skills
Portfolio: reproducible code, documented assumptions, small deployments.
CV: measurable impact over “responsible for”.
ATS alignment: mirror job ad keywords in your skills section.
Interview prep: be ready for system design, trade-offs, and case studies.
UK-Specific Hiring Signals
GenAI skills mentions in job ads are climbing.
AI Engineer roles remain high on LinkedIn’s UK “Jobs on the Rise” lists.
Communication & collaboration skills remain consistently requested.
Suggested 12-Week Learning Path
Weeks 1–3: Python, SQL, data foundations.
Weeks 4–6: Core ML fundamentals.
Weeks 7–8: Deep learning specialism.
Weeks 9–10: Cloud & MLOps deployment.
Weeks 11–12: LLMs & RAG mini-project.
FAQs
What is the most in-demand AI skill in the UK?
Python with core machine learning remains the most consistently requested pairing, with LLMs rising fast.
Do employers ask for prompt engineering?
Yes—particularly in roles focused on GenAI.
Are soft skills still important?
Absolutely. Communication is one of the most cited skills in UK postings.
Which cloud should I learn first?
AWS or Azure are safest bets for UK employers, with GCP also valued.
Final Checklist
Headline & About: clear focus on AI specialisms.
CV bullets: measurable outcomes.
Skills: Python, SQL, PyTorch/TensorFlow, cloud, MLOps, LLMs, NLP/CV, communication.
Portfolio: 2–3 end-to-end projects, one GenAI/RAG.
Keywords: mirror job ad language for ATS.
Conclusion
If you want to be competitive for UK AI roles, focus your learning & evidence around these ten skills: Python, ML fundamentals, deep learning frameworks, SQL, cloud, MLOps, LLMs/RAG, NLP, computer vision, & communication/storytelling. That combination aligns cleanly with how employers write their job ads today—and with where the UK market is heading as GenAI matures.
