Full Stack Developer

Get Staffed Online Recruitment Careers
Birmingham
1 year ago
Applications closed

Related Jobs

View all jobs

Data Scientist

Senior Data Scientist

Senior Data Scientist

Data Scientist

Full-Stack Data Scientist: NLP & Production ML

Data Scientist (Full Stack)


Full Stack Developer

Our client's goal is simple: to make vehicle movement easy. They are on their way to creating the leading end-to-end movement solution in the UK for their customers (such as Hertz and The AA), delivered by their network of 600+ drivers and transport agents across the UK.

Since going live in April 2018, they have acquired over 80 clients, many of whom can claim to be amongst the largest players in the UK automotive industry. They are already one of the largest competitors in their space but have ambitions to grow much further and they are crazy about sustainability. To date they have saved fleets over 9,000,000 tonnes of CO2.

They are seeing their hard work paying off as they have won seven awards, including Best Fleet Software three years in a row, a highly commended wellbeing award, two innovation awards, and one outstanding product of the year award.

About The Position

Development and management of our client's productions systems moved in-house a little under four years ago, since then they have been building out a seriously talented team to grow their platform. Their core application is hosted in AWS on Linux containers, developed in C#.NetCore with a SPA Angular front-end along with an accompanying mobile app on iOS & Android. They also have some data processing services and machine learning workloads.

As they are in their ‘growth phase' you will be joining this expanding IT team. They are look...

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.