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Featured Jobs
Senior Machine Learning Research Engineer
Closing Date: 29 March 2026 Salary: £64,490 - £86,255 Location: Cambridge - Triangle/Hybrid (2 days per week in the office) Contract: Permanent Hours: Full Time (35 hours per week) Shape the future of AI-powered learning solutions with Cambridge University Press & Assessment, a world-leading academic publisher and assessment organisation, and a proud part of the University of Cambridge. This is...
Cambridge University Press & Assessment
Cambridge
Artificial Intelligence Offerings Lead Architect
Artificial Intelligence Offerings Lead Architect Location: Basingstoke, flexible but, expected to travel to other FJ and customer sites Our purpose is to make the world more sustainable by building trust in society through innovation. Are you passionate about shaping and growing the future of AI for our business? Do you have a track record in AI and data with an...
Fujitsu
Basingstoke
Artificial Intelligence (AI) Consultant
ABS is seeking an exceptional Artificial Intelligence (AI) Consultant to join us full-time in our AI Practice Team, Europe. In this role, you will coordinate and support the full lifecycle of AI projects, keeping delivery aligned between clients and our technical teams across strategy, solutions, and execution. You will stay close to day-to-day project activity, ensuring requirements are clearly understood,...
American Bureau of Shipping
Warrington
Artificial Intelligence (AI) Consultant
ABS is seeking an exceptional Artificial Intelligence (AI) Consultant to join us full-time in our AI Practice Team, Europe. In this role, you will coordinate and support the full lifecycle of AI projects, keeping delivery aligned between clients and our technical teams across strategy, solutions, and execution. You will stay close to day-to-day project activity, ensuring requirements are clearly understood,...
If you are job hunting in AI right now it can feel like you are drowning in tools. Every week there is a new framework, a new “must-learn” platform or a new productivity app that everyone on LinkedIn seems to be using. The result is predictable: job seekers panic-learn a long list of tools without actually getting better at delivering outcomes.
Here is the truth most hiring managers will quietly agree with. They do not hire you because you know 27 tools. They hire you because you can solve a problem, communicate trade-offs, ship something reliable and improve it with feedback. Tools matter, but only in service of outcomes.
So how many AI tools do you actually need to know? For most AI job seekers: fewer than you think. You need a tight core toolkit plus a role-specific layer. Everything else is optional.
This guide breaks it down clearly, gives you a simple framework to choose what to learn and shows you how to present your toolset on your CV, portfolio and interviews.
Hiring managers do not start by reading your CV line-by-line. They scan for signals. In AI roles especially, they are looking for proof that you can ship, learn fast, communicate clearly & work safely with data and systems. The best applications make those signals obvious in the first 10–20 seconds.
This guide breaks down what hiring managers typically look for first in AI applications in the UK market, how to present it on your CV, LinkedIn & portfolio, and the most common reasons strong candidates get overlooked. Use it as a checklist to tighten your application before you click apply.
Artificial intelligence is no longer a future concept. It is already reshaping how businesses operate, how decisions are made, and how entire industries compete. From finance and healthcare to retail, manufacturing, defence, and climate science, AI is embedded in critical systems across the UK economy.
Yet despite unprecedented demand for AI talent, employers continue to report severe recruitment challenges. Vacancies remain open for months. Salaries rise year on year. Candidates with impressive academic credentials often fail technical interviews.
At the heart of this disconnect lies a growing and uncomfortable truth:
Universities are not fully preparing graduates for real-world AI jobs.
This article explores the AI skills gap in depth—what is missing from many university programmes, why the gap persists, what employers actually want, and how jobseekers can bridge the divide to build a successful career in artificial intelligence.
Changing career into artificial intelligence in your 30s, 40s or 50s is no longer unusual in the UK. It is happening quietly every day across fintech, healthcare, retail, manufacturing, government & professional services. But it is also surrounded by hype, fear & misinformation.
This article is a realistic, UK-specific guide for career switchers who want the truth about AI jobs: what roles genuinely exist, what skills employers actually hire for, how long retraining really takes & whether age is a barrier (spoiler: not in the way people think).
If you are considering a move into AI but want facts rather than Silicon Valley fantasy, this is for you.
Artificial intelligence is now embedded across almost every sector of the UK economy. From fintech and healthcare to retail, defence and climate tech, organisations are competing for AI talent at an unprecedented pace.
Yet despite the volume of AI job adverts online, many employers struggle to attract the right candidates. Roles are flooded with unsuitable applications, while highly capable AI professionals scroll past adverts that feel vague, inflated or disconnected from reality.
In most cases, the issue isn’t a shortage of AI talent — it’s the quality of the job advert.
Writing an effective AI job ad requires more care than traditional tech hiring. AI professionals are analytical, sceptical of hype and highly selective about where they apply. A poorly written advert doesn’t just fail to convert — it actively damages your credibility.
This guide explains how to write an AI job ad that attracts the right people, filters out mismatches and positions your organisation as a serious employer in the AI space.
If you are a software engineer, data scientist or analyst looking to move into AI or you are a UK undergraduate or postgraduate in computer science, maths, engineering or a related subject applying for AI roles, the maths can feel like the biggest barrier. Job descriptions say “strong maths” or “solid fundamentals” but rarely spell out what that means day to day.
The good news is you do not need a full maths degree worth of theory to start applying. For most UK roles like Machine Learning Engineer, AI Engineer, Data Scientist, Applied Scientist, NLP Engineer or Computer Vision Engineer, the maths you actually use again & again is concentrated in a handful of topics:
Linear algebra essentials
Probability & statistics for uncertainty & evaluation
Calculus essentials for gradients & backprop
Optimisation basics for training & tuning
A small amount of discrete maths for practical reasoning
This guide turns vague requirements into a clear checklist, a 6-week learning plan & portfolio projects that prove you can translate maths into working code.
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