Senior Machine Learning Engineer

Gravitas Recruitment Group (Global) Ltd
Manchester
1 month ago
Applications closed

Related Jobs

View all jobs

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior Machine Learning Engineer

Senior MLOps Engineer

We’re hiring a Senior MLOps Engineer to join a fast-growing AI consultancy based in Manchester City Centre (3 days on-site). If you're passionate about productionising machine learning and want to work on real-world AI challenges, this could be the role for you.


What you’ll be doing:

  • Collaborating with clients to deploy and scale ML models
  • Working closely with DS/ML teams in a highly collaborative, pair-programming environment
  • Taking ownership of features and running sprint planning sessions
  • Contributing to projects across research, prototyping, and production

What we’re looking for:

  • 3+ years of experience in a Data Science / Machine Learning / Engineering role
  • Strong Python skills and experience writing production‑grade code
  • Familiarity with ML frameworks (TensorFlow, PyTorch, Keras, SKLearn)
  • Proficiency with Git, Unix/Linux, Docker
  • Cloud experience (AWS, Azure, or similar)

Nice to have:

  • Open source contributions
  • Curiosity for learning new tech
  • Personal L&D budget
  • 25 days holiday (rising to 30)
  • Pension

Seniority level

Mid‑Senior level


Employment type

Full‑time


Job function

Information Technology


Industries

Data Infrastructure and Analytics, Software Development, and IT Services and IT Consulting


#J-18808-Ljbffr

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.

Careers

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.

Jobs

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.

Education

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.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.