Machine Learning Engineer

Xcede
Newcastle upon Tyne
1 month ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer:


Xcede has started working with a leading AI solutions company. Wanting to turn advanced machine learning into dependable production systems, you will shape technical delivery and drive real-world impact for clients.


This role involves designing and delivering robust machine-learning systems from concept to live operation, develop shared approaches that speed up future delivery, work closely with multidisciplinary teams to address complex client needs, take ownership of technical definition and system design, establish best practices for scalable deployment, and provide clear technical guidance to stakeholders across a range of audiences.


Requirements:

  • You have end-to-end experience with machine-learning workflows, including taking trained models and integrating them into live systems using modern ML tooling
  • You are highly proficient in Python and apply sound engineering principles when designing and building software
  • You have practical experience designing and operating systems within large-scale cloud environments, with an understanding of secure and resilient architecture.
  • You have experience packaging applications for repeatable deployment and operating them reliably across scalable runtime environment
  • You have a solid grasp of the mathematical and conceptual foundations that underpin modern machine-learning methods
  • You communicate clearly and effectively, supporting technical teams while providing trusted guidance to a wide range of stakeholders
  • You are comfortable operating in dynamic settings and take ownership of defining, solving, and delivering end-to-end solutions


If you are interested in this or other ML Engineer positions, please contact Gilad Sabari @ |

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.