Machine Learning Engineer

Data Science Festival
City of London
1 month ago
Create job alert

We are currently looking for a Machine Learning Engineer to join our client’s data team. This is a hands‑on role where you’ll design and build robust data pipelines, transform ML prototypes into production‑ready systems, and champion MLOps best practices across the business. As a Machine Learning Engineer, you’ll play a crucial role in ensuring our clients’ data and AI strategy scales effectively, directly influencing the way millions of people engage with their products every day.


The Opportunity

This is a unique chance to combine data engineering with machine learning in a high‑impact environment. You’ll work closely with analysts, data engineers and stakeholders, ensuring models are reliable, scalable, and production-ready. Unlike many roles in the tech sector, this Machine Learning Engineer role gives you the visibility of seeing your work applied at scale, powering decision‑making and user experiences for a vast audience.


Your day‑to‑day will include:

  • Building and maintaining end‑to‑end data pipelines and feature engineering workflows.
  • Deploying and monitoring ML models in production using tools such as MLflow, Vertex AI, or Azure ML.
  • Driving best practices in MLOps, including CI/CD, experiment tracking, and model governance.
  • Supporting the data warehouse and ensuring data quality, governance, and accessibility.
  • Collaborating with cross‑functional teams to deliver trusted datasets and insights.

What’s in it for you?

  • Competitive salary with annual reviews.
  • Hybrid working model offering flexibility.
  • Generous holiday allowance that increases with service.
  • Onsite wellness facilities, subsidised meals, and gym access.
  • Access to wellbeing support services and employee assistance programmes.
  • Clear career progression and opportunities to work with cutting‑edge tech.

Skills and Experience

  • Degree in Computer Science, Engineering, Mathematics, or a related field.
  • Strong knowledge of Python and SQL.
  • Hands‑on experience with cloud platforms (GCP or Azure) and Databricks.
  • Familiarity with deploying ML workflows using MLflow, Vertex AI, or Azure ML.

Nice-to-have:

  • Experience with Spark, CI/CD pipelines, and orchestration tools.
  • Knowledge of Elasticsearch or digital/web analytics platforms.
  • Understanding of the full machine learning lifecycle, from experimentation to evaluation.

If you would like to be considered for the Machine Learning Engineer role and feel you’d be an ideal fit for our team, please click the Apply button to submit your CV. We look forward to hearing from you!


#J-18808-Ljbffr

Related Jobs

View all jobs

Machine Learning Engineer / MLOps Engineer

Machine Learning Engineering Lead

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

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.

Careers

How Many Machine Learning Tools Do You Need to Know to Get a Machine Learning Job?

Machine learning is one of the most exciting and rapidly growing areas of tech. But for job seekers it can also feel like a maze of tools, frameworks and platforms. One job advert wants TensorFlow and Keras. Another mentions PyTorch, scikit-learn and Spark. A third lists Mlflow, Docker, Kubernetes and more. With so many names out there, it’s easy to fall into the trap of thinking you must learn everything just to be competitive. Here’s the honest truth most machine learning hiring managers won’t say out loud: 👉 They don’t hire you because you know every tool. They hire you because you can solve real problems with the tools you know. Tools are important — no doubt — but context, judgement and outcomes matter far more. So how many machine learning tools do you actually need to know to get a job? For most job seekers, the real number is far smaller than you think — and more logically grouped. This guide breaks down exactly what employers expect, which tools are core, which are role-specific, and how to structure your learning for real career results.

Jobs

What Hiring Managers Look for First in Machine Learning Job Applications (UK Guide)

Whether you’re applying for machine learning engineer, applied scientist, research scientist, ML Ops or data scientist roles, hiring managers scan applications quickly — often making decisions before they’ve read beyond the top third of your CV. In the competitive UK market, it’s not enough to list skills. You must send clear signals of relevance, delivery, impact, reasoning and readiness for production — and do it within the first few lines of your CV or portfolio. This guide walks you through exactly what hiring managers look for first in machine learning applications, how they evaluate CVs and portfolios, and what you can do to improve your chances of getting shortlisted at every stage — from your CV and LinkedIn profile to your cover letter and project portfolio.

Careers Jobs

MLOps Jobs in the UK: The Complete Career Guide for Machine Learning Professionals

Machine learning has moved from experimentation to production at scale. As a result, MLOps jobs have become some of the most in-demand and best-paid roles in the UK tech market. For job seekers with experience in machine learning, data science, software engineering or cloud infrastructure, MLOps represents a powerful career pivot or progression. This guide is designed to help you understand what MLOps roles involve, which skills employers are hiring for, how to transition into MLOps, salary expectations in the UK, and how to land your next role using specialist platforms like MachineLearningJobs.co.uk.

🍪 We use cookies to enhance your browsing experience on our website. By continuing to use this site, you consent to the use of cookies as described in our Cookie Policy. You can review our Cookie Policy for more information.