Director of Machine Learning & AI

Army Marketing
City of London
4 months ago
Applications closed

Related Jobs

View all jobs

Director, Machine Learning Science - Recommendations & Relevance

Director of Machine Learning

Senior Data Scientist

Director, Data Science - Measurement & Optimization

Principal Data Scientist

Data Scientist II, PLS Analytics

Overview

Location: London / Remote

Package: Strong Salary + Benefits

We\'re partnered with a fast-growing tech business that\'s investing heavily in its AI capability. They\'re looking for a proven ML/AI leader who can take charge of multiple high-performing teams and set the direction for how Machine Learning is built and scaled across the organisation.

This isn\'t about \'experiments in the corner\', it\'s about shaping strategy, leading delivery, and putting advanced ML systems into production where they make a measurable difference.

What You\'ll Be Doing
  • Leading and scaling cross-functional teams (Data Science, ML Engineering, Software Engineering, Managers).
  • Defining the ML/AI roadmap and aligning it to business outcomes.
  • Driving best practice in MLOps, deployment, observability, and automation.
  • Working with cutting-edge approaches: deep learning frameworks, foundation models, knowledge graphs, etc.
  • Being the voice of ML/AI leadership, setting standards, mentoring, and building a culture of delivery.
What They\'re Looking For
  • You\'ve built and run ML teams at scale, and delivered systems into production, not just research.
  • Technical depth in modern ML/AI (from LLMs to advanced ML infra).
  • Strong grasp of cloud-native, containerised environments and modern engineering practice.
  • Credibility with both exec stakeholders and engineers, you can set strategy and still talk technical detail when needed.
  • Bonus if you\'ve operated in complex, regulated, or high-transaction environments.
Why This Role
  • Serious investment in AI/ML
  • High autonomy and direct impact on how the organisation uses ML at scale
  • Forward-looking culture: growth budget for self-development, modern tech stack, flexibility in where and how you work.
  • A chance to define and own the ML strategy in a business that sees it as a competitive edge, not a side project.

Director of Machine Learning & AI

RSG Plc is acting as an Employment Agency in relation to this vacancy.


#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.

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.