Be at the heart of actionFly remote-controlled drones into enemy territory to gather vital information.

Apply Now

Machine Learning Engineer - GenAI

Experian Ltd
London
2 days ago
Create job alert

Company Description Experian is a global data and technology company, powering opportunities for people and businesses around the world. We help to redefine lending practices, uncover and prevent fraud, simplify healthcare, create marketing solutions, and gain deeper insights into the automotive market, all using our unique combination of data, analytics and software. We also assist millions of people to realize their financial goals and help them save time and money.
We operate across a range of markets, from financial services to healthcare, automotive, agribusiness, insurance, and many more industry segments.
We invest in people and new advanced technologies to unlock the power of data. As a FTSE 100 Index company listed on the London Stock Exchange (EXPN), we have a team of 22,500 people across 32 countries. Our corporate headquarters are in Dublin, Ireland. Learn more at experianplc.com.
Job Description The Generative AI Centre of Expertise (GenAI CoE) at Experian exists to improve our products, our internal processes and our daily work through GenAI and process automation. The team is a mix of ML engineers, data scientists and product owners, who are dedicated to the next wave of innovation using GenAI.
Reporting into our Head of Machine Learning Product Engineering you will drive the delivery of concepts and ideas into products and services that Experian can take to their customers, whether that be businesses or direct to consumers. To do this, we build upon the outcomes of our experiments to meet the product requirements - considering performance, maintainability, and scalability. We, alongside the data scientists in the team, collaborate with a range of stakeholders.
You will:
Partner with teams across the organisation to develop GenAI solutionsfrom early experimentation to full-scale productionpotentially including DevOps work where needed.
Architect and build high-performant solutions, which may involve traditional ML modelling or large datasets, as well as GenAI.
Discover and introduce new technologies to the team, staying up to date with the latest approaches that enable the next generation of Experian's products with GenAI and ML.
Spend 10% of your work time on learning and sharing expertise on generative-AI technologies.
Qualifications Have a degree or equivalent qualification in a STEM subject.
Familiar with Unix environments.
Exposure to at least one other programming language besides Python.
Proficiency in object-oriented programming (OOP), SOLID principles, and test-driven development (TDD).
Proficiency with Docker and experience working with container orchestration tools such as Kubernetes, Docker Swarm, or cloud-based alternatives.
Comfort working across the full development stack, especially for prototyping.
Passion for applying GenAI and machine learning across diverse domains and throughout the full project lifecycle.
Experience with common ML approaches (e.g., LLMs, GBMs, deep learning) and typical software architectures.
Experience as a lead developer solving complex problems at scale.
Experience mentoring junior engineers.
Familiarity with various machine learning frameworks and toolkits (e.g., scikit-learn, XGBoost, TensorFlow).
Hands-on experience building GenAI solutions using patterns such as Retrieval-Augmented Generation (RAG) or fine-tuning Large Language Models (LLMs).
Greater familiarity with AWS compared to other cloud computing platforms.
Have experience developing REST APIs.
Additional Information Benefits package includes:
Hybrid working
Great compensation package and discretionary bonus
Core benefits include pension, bupa healthcare, sharesave scheme and more
25 days annual leave with 8 bank holidays and 3 volunteering days. You can purchase additional annual leave.
We take our people agenda very seriously and focus on what matters; DEI, work/life balance, development, authenticity, collaboration, wellness, reward & recognition, volunteering... the list goes on. Experian's people first approach is award-winning; World's Best Workplaces 2024 (Fortune Top 25), Great Place To Work in 24 countries, and Glassdoor Best Places to Work 2024 to name a few. Check out Experian Life on social or our Careers Site to understand why.
Experian is proud to be an Equal Opportunity and Affirmative Action employer. Innovation is an important part of Experian's DNA and practices, and our diverse workforce drives our success. Everyone can succeed at Experian and bring their whole self to work, irrespective of their gender, ethnicity, religion, colour, sexuality, physical ability or age. If you have a disability or special need that requires accommodation, please let us know at the earliest opportunity.
Experian Careers - Creating a better tomorrow together
Find out what its like to work for Experian by clicking here
Experian Careers - Creating a better tomorrow together
Find out what its like to work for Experian by clicking here

TPBN1_UKTJ

Related Jobs

View all jobs

Machine Learning Engineer

Machine Learning Engineer (LLMs & AI Agents)

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.

Why Machine Learning Careers in the UK Are Becoming More Multidisciplinary

Machine learning (ML) has moved from research labs into mainstream UK businesses. From healthcare diagnostics to fraud detection, autonomous vehicles to recommendation engines, ML underpins critical services and consumer experiences. But the skillset required of today’s machine learning professionals is no longer purely technical. Employers increasingly seek multidisciplinary expertise: not only coding, algorithms & statistics, but also knowledge of law, ethics, psychology, linguistics & design. This article explores why UK machine learning careers are becoming more multidisciplinary, how these fields intersect with ML roles, and what both job-seekers & employers need to understand to succeed in a rapidly changing landscape.

Machine Learning Team Structures Explained: Who Does What in a Modern Machine Learning Department

Machine learning is now central to many advanced data-driven products and services across the UK. Whether you work in finance, healthcare, retail, autonomous vehicles, recommendation systems, robotics, or consumer applications, there’s a need for dedicated machine learning teams that can deliver models into production, maintain them, keep them secure, efficient, fair, and aligned with business objectives. If you’re hiring for or applying to ML roles via MachineLearningJobs.co.uk, this article will help you understand what roles are typically present in a mature machine learning department, how they collaborate through project lifecycles, what skills and qualifications UK employers look for, what the career paths and salaries are, current trends and challenges, and how to build an effective ML team.

Why the UK Could Be the World’s Next Machine Learning Jobs Hub

Machine learning (ML) is becoming essential to industries across the globe—from finance and healthcare to retail, logistics, defence, and the public sector. Its ability to uncover patterns in data, make predictions, drive automation, and increase operational efficiency has made it one of the most in-demand skill sets in the technology world. In the UK, machine learning roles—from engineers to researchers, product managers to analysts—are increasingly central to innovation. Universities are expanding ML programmes, enterprises are scaling ML deployments, and startups are offering applied ML solutions. All signs point toward a surging need for professionals skilled in modelling, algorithms, data pipelines, and AI systems. This article explores why the United Kingdom is exceptionally well positioned to become a global machine learning jobs hub. It examines the current landscape, strengths, career paths, sector-specific demand, challenges, and what must happen for this vision to become reality.