Lead Machine Learning Engineer

London
10 months ago
Applications closed

Related Jobs

View all jobs

Lead Machine Learning Engineer, AI

Lead Machine Learning Engineer, Gen AI

Staff Machine Learning Engineer

Staff Machine Learning Engineer

Senior Machine Learning Engineer (Platform) - Bristol

Senior Machine Learning Engineer (Platform) - Exeter

Lead ML Engineer
London - Hybrid (1 day per week in office)
£80,000 - £100,000 DOE + Bonus (up to 15%) + Excellent Pension + Car Scheme + Technology Benefits + EAP Programme + Flexible working

This is an incredible opportunity for a Lead ML Engineer to join a fast paced and forward-thinking business always looking to innovate and lead from the front in the technology world.

The company are a leading organisation in the energy sector, dedicated to delivering innovative solutions and improving operational efficiency. As part of their Data Science team, you will be at the forefront of cutting-edge projects, helping to shape the future of data-driven decision-making and machine learning infrastructure.

In this role, you will lead machine learning projects from concept to production, develop platform tools, and collaborate with data scientists to build data pipelines. You'll mentor junior team members, work with IT teams to advance projects, and improve deployment processes. Additionally, you'll design and maintain cloud infrastructure, ensure high-quality code, and participate in code reviews.

The ideal candidate will have proven experience in a similar role in some combination of Software, Data or DevOps and strong experience working with ML models. You will be an expert with Python and associated libraries (Pandas, scikit-learn etc.), have strong Azure DevOps exposure (Terraform, Docker, Kubernetes) and high proficiency in SQL.

An incredible opportunity for a confident and commercial ML Engineer to lead from the front working with cutting edge technology and driving company growth.

The Role:

Lead machine learning projects from concept to production.
Develop platform tools and collaborate with data scientists to build data pipelines.
Mentor junior team members and support their technical growth.
Work closely with IT teams to advance project goals and improve deployment processes.
Design and maintain cloud infrastructure to support machine learning initiatives.
The Person:

Proficiency in Python, including libraries such as Pandas and scikit-learn, and strong SQL skills.
Deep understanding of software engineering best practices
Experience with tools like Azure, Azure ML, GitHub Actions, Terraform, Packer, Airflow, Docker, and Kubernetes
Expertise in Linux/Windows VM administration.

Reference Number: BBBH(phone number removed)

To apply for this role or to be considered for further roles, please click "Apply Now" or contact Ryan McIntyre at Rise Technical Recruitment.

Rise Technical Recruitment Ltd acts an employment agency for permanent roles and an employment business for temporary roles.

The salary advertised is the bracket available for this position. The actual salary paid will be dependent on your level of experience, qualifications and skill set. We are an equal opportunities employer and welcome applications from all suitable candidates

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.