Computer Vision Engineer

Experis Scotland
Leeds
3 months ago
Applications closed

Related Jobs

View all jobs

Computer Vision Engineer

Robotics / Computer Vision Engineer

Senior Computer Vision Engineer

Robotics / Computer Vision Engineer

Senior Computer Vision Engineer

C++ Computer Vision AI Engineer

Computer Vision Lead – UK Remote (with travel to London)

Salary: £125,000


We’re hiring a Computer Vision Lead to join a cutting-edge AI company that’s redefining how computer vision is applied in real-world production environments. This is a high-priority hire and a rare opportunity to lead CV strategy and execution at the heart of a fast-scaling tech team.


You’ll be the technical authority on computer vision, working alongside a team of 4 engineers and collaborating closely with leadership. While people management is handled by another team member, you’ll own the technical direction and be the go-to expert for CV.


What You’ll Be Doing

  • Lead the development and deployment of production-grade computer vision models.
  • Own the CV roadmap and guide the team through complex technical challenges.
  • Collaborate with engineering leads and founders to shape product and tech strategy.
  • Contribute to hiring and mentoring as the team scales.


Essential Skills & Experience

  • Deep expertise in Computer Vision, especially in Object Detection.
  • Strong hands-on experience with PyTorch, TensorFlow, and Transformer.
  • Proven track record of delivering CV solutions in production environments.
  • Ability to communicate technical concepts clearly across teams.
  • UK-based and able to travel to London when required.

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.