Data Science and AI Engineering Manager

City of London
3 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineering Manager, Gen AI

Lead Data Scientist

Lead Data Scientist

Lead Data Scientist

Principal Data Scientist I - Agentic Systems

Data Science Manager

Data Science and AI Engineering Manager

Salary: £100,000 - £125,000 + bonus
Location: London / Hybrid

Data Idols are working with a major UK brand embarking on an exciting transformation to embed AI at the heart of its technology strategy. This is a brand-new role, a chance to build and lead an AI Engineering function from the ground up within an established enterprise.

The Opportunity

You'll set the strategy and lead delivery for a new AI Engineering area, responsible for developing practical AI solutions, from agentic systems and intelligent agents to automation tools and advanced operational AI. You'll also drive the adoption of Gen AI and generative capabilities across the organisation, ensuring they are translated into real, scalable business impact.

This is not a research role; it's about turning AI into real-world value. You'll consolidate existing ML and AI Ops teams under one function, shaping how the organisation delivers, governs, and scales AI, including generative and agentic technologies, end-to-end.

Skills and Experience

Proven leadership experience in Gen AI delivery
Hands-on technical understanding of modern AI tooling, MLOps, and model deployment and generative/agentic AI frameworks
Commercial mindset, experience bringing AI, including generative, use cases into production
Strong communication skills and the ability to build and scale new teams
Passion for AI innovation and keeping up with GenAI industry advancesIf you are looking for a new challenge, please submit your CV for initial screening and more details.

Data Science and AI Engineering Manager

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.