Senior Frontend Developer

Edinburgh
1 year ago
Applications closed

Related Jobs

View all jobs

Principal Data Scientist & Machine Learning Researcher

Senior Machine Learning Research Engineer

Senior Machine Learning Engineer

Senior Data Scientist

Senior Machine Learning Scientist

Senior Machine Learning Engineer

Are you a senior level software engineer looking for the autonomy to thrive within a start up environment?

Would you like to join an organisation that truly enables it's employees to make business critical decisions that will have tangible results in real time?

You have the opportunity to join a Scottish based organisation who are leading the way within innovation in their field as they scale rapidly for the next phase of growth

You will work on a complex range of AI products designed to revolutionise an industry for both household customers and large corporate businesses through cutting edge computer vision, machine learning models and web applications.

Within your daily duties you will have the opportunity to make high level technical decisions and take the lead on cutting edge projects using key tech inclusive of React, TypeScript, Tailwind and React Native.

You will be joining at an exciting time as initial core products have been built and large data sets are due to be added.  You will have the backing and autonomy to help build a new permanent function within the business to move them to the next phase of development.

Curious?  Contact me for more details on (phone number removed),  (url removed) or message me directly on LinkedIn

Subscribe to Future Tech Insights for the latest jobs & insights, direct to your inbox.

By subscribing, you agree to our privacy policy and terms of service.

Industry Insights

Discover insightful articles, industry insights, expert tips, and curated resources.

How Many AI Tools Do You Need to Know to Get an AI Job?

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.

What Hiring Managers Look for First in AI Job Applications (UK Guide)

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.

The Skills Gap in AI Jobs: What Universities Aren’t Teaching

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.