Senior Machine Learning Engineer

G MASS
London
1 month ago
Create job alert

G MASS Consulting are supporting a leading Audit and Advisory business. We’re looking for a Machine Learning Engineer to shape and scale their pricing technology. In this role, you’ll design and own ML platforms that streamline pricing workflows, support rapid model deployment, and ensure models perform reliably at scale. Partnering with Data Science, Actuarial, and Product teams.

Responsibilities:

  • Build and support ML lifecycle tooling for model deployment, monitoring, and alerting
  • Maintain and improve the Kubeflow environment for Data Scientists and Actuaries
  • Create pricing analytics tools to accelerate impact analysis and reduce manual work
  • Collaborate with pricing and product teams to deliver high-impact tooling
  • Communicate complex concepts clearly to technical and non-technical audiences

Requirements

  • Bachelor’s or Master’s degree in Statistics, Data Science, Computer Science, or a related field
  • Strong experience managing the full ML model lifecycle (batch and online)
  • Solid understanding of statistical methods, including GLMs and modern ML techniques
  • Proven ability to build and deploy production-quality Python applications (pandas, scikit-learn)
  • Experience with DevOps and ML tooling, including Kubernetes, Docker, CI/CD, and git-based workflows
  • Familiarity with cloud platforms (AWS) and cloud data warehouses (Snowflake/SQL)

Benefits

Salary: to be discussed, depending on experience

Length: 12 months, with the view to extend

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

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.