Machine Learning Engineer

IAG Loyalty
London
11 months ago
Applications closed

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer

Machine Learning Engineer - Bristol

Audio Machine Learning Engineer

Senior Machine Learning Engineer

Who we are

We're the people behind global loyalty currency, Avios, and home to three ambitious, growing businesses; IAG Loyalty, BA Holidays and The Wine Flyer. Each business has its own goals and strategy, but collectively we create brilliant experiences for our global customers.

We're on a truly exciting journey of growth and transformation - we're going places! This is where you come in.

The opportunity

You'll be joining our Data Products team, working closely with teams that are aligned to value streams. Our team exists to drive success across Commercial, Customer, Finance and Product domains. We're on a mission to make our data available and usable in one place to deliver powerful data solutions that optimise performance and supercharge decision-making.

We're putting data at the heart of what we do to deliver super experiences to our customers and colleagues with machine learning (ML) foundational to this mission. We are looking to embed a centralised AI / ML platform to make our colleagues' lives easier when creating value for the company in a standardised way. This platform will enable safe, efficient and reliable training and deployment of ML models.

We are looking for an experienced ML Engineer to help us scale our capabilities to meet the needs of our team and our company's goals. You'll enjoy operating in a hybrid data science / ML engineering space, with an interest in platform / operations.

You will be joining in the early stages of our journey as we build out the infrastructure, tools and processes. We're migrating to Snowflake platform along with other tooling as part of the modern data stack such as AWS (SageMaker). In this role, you will be using ground-breaking technology to build and support world-class AI-based products, batch in the near term, and real-time in the future.

What you'll get up to

  • Contribute to the design, development, and deployment of our ML platform
  • Define and implement best practices for training, evaluating, and deploying machine learning models at scale
  • Collaborate with software engineers, data engineers, and data scientists to develop end-to-end AI products & solutions
  • Extend and maintain our self-service platform to help our data science teams quickly productionise models following MLOps best practices

What we need from you

  • Hands-on experience in data science and machine learning engineering, with a proven track record from design to deployment.
  • Extensive knowledge of software development with Python, SOLID principles
  • Understanding of machine learning techniques, such as supervised and unsupervised learning, MLOps, and data engineering
  • Infrastructure as Code - Terraform or equivalent
  • Practical knowledge of AWS, Snowflake, GCP or Azure
  • Experience with or equivalent to Snowflake Cortex / AWS Bedrock, AWS MSK, Snowpipe Streaming, Snowpark Container Services

The role has a blended base, between our Central London office and home. We trust you to make the right decision about the type of work that is best done in each location. We expect you'll achieve a 50/50 split between the two and there may be times where you'll want to visit our other locations to tell their stories, too (fully reimbursed).

We might not be right for you if:

  • You value perfection over fast iteration and progress; IAG Loyalty moves fast, we learn and iterate as we go; our environment isn't right for everyone.

If you think you have what it takes but don't meet every single point above, please do still apply. We'd love to chat and see if you could be a great fit.

And in return? You'll get access to a whole host of travel, Avios, healthcare benefits and more.Find out more here.

Equity, Diversity and Inclusion at IAG Loyalty

Our vision, 'to create the world's most rewarding experiences,' applies not only to our customers but for our colleagues too. It's about taking belonging seriously, actively fostering a culture where everyone feels welcomed and valued by embracing diverse identities, personal histories, and perspectives.

This commitment makes IAG Loyalty a rewarding place to work and enhances our ability to solve complex problems, drive innovation, and better serve our customers and communities.

Please let us know if we can make any reasonable adjustments to support your interview process with us.#J-18808-Ljbffr

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 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.

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.

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.