Machine Learning Engineer

The Rank Group
London
2 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer / MLOps Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Job Description

We want to expand our Data Science function further within our well-established strong data-driven Centralised Analytical department. Our Data Science mission is to build machine learning models in the production environment relative to Marketing, Customer Insights, and Safer Gambling and establish a strong culture of data-driven decision-making in our organisation's strategy. 

We are looking for an experienced Machine Learning Engineer to support the delivery of ML products. You will work closely with the Data Science team, to understand the data and product requirements. You will also collaborate the Data & Ops team, to stay on top of key changes that may impact the ML frameworks and to own the release of ML products. We use Azure Databricks as our platform.

To be successful in the role, you will need to be experienced in cross-team collaboration to deliver data science projects, whilst promoting best practices. You will be proactive in identifying and communicating data-related issues and will act as the interface between Data Science and Data & Ops teams.

The Data Science department is currently a smaller team, with an ambition to grow, with a mix of a Data Scientists and ML engineers. Therefore, it is an excellent opportunity to grow, contribute and challenge yourself.

Core Responsibilities

  • Development and maintenance of the ML data pipeline, with the proper quality controls and contingency plans in place
  • Model deployment & serving, ensuring that solutions align with internal best practices and have a high degree of automation
  • Cost control, having both the data production and solution deployment as efficiently as possible
  • Cross-team collaboration, communicating all key elements that impact the well-functioning of the ML solutions


Qualifications

  • Postgraduate degree in a relevant discipline (e.g. IT, STEM, Maths, Computer Science) or equivalent experience
  • Good data modelling, software engineering knowledge, and strong knowledge of ML packages and frameworks
  • Skilful in writing well-engineered code using Spark, and advanced SQL and Python coding skills
  • Experienced in working with Azure Databricks
  • Proven experience working with Data Scientists to deliver best in-class solutions for model deployment and monitoring
  • Great technical and communication skills, with a high degree of proactivity
  • Passion for learning and keeping abreast of new technologies and data models



Additional Information

#LI-IZ1 #LI-Hybrid

Join us to unlock benefits and opportunities that will boost your career journey in a vibrant, inclusive and fulfilling work environment – so you can #BeYourself

Wellbeing@Rank is important... From hybrid working and colleague support networks to menopause support and weekly PepTalks, we’re here for you.  

We’ll also invest in your growth by providing development opportunities, leadership training and cutting-edge industry certifications so you have the tools and resources to help you work, win and grow with us. 

Immerse yourself in new cultures and gain international exposure through our global business. Collaborate with colleagues from around the globe.  

From pensions to bonus schemes, and private medical insurance to life insurance – we've got you covered. 

*Our benefits vary by brand and/or location. Please have a chat with your local Talent Acquisition specialist to find out what’s in place in your location.    

The Rank Group are committed to being an inclusive employer, ensuring that we better understand and meet the needs and requirements of our candidates and customers. 

We aim to do this by facilitating fair and equal access to our services. If you require a reasonable adjustment to be made, please reach out to let us know ahead of your interview. 

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.