At a Glance
- Tasks: Design and build production-ready machine learning systems to transform risk understanding.
- Company: Leading insurer investing in data science and innovative AI solutions.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Collaborative environment with exciting projects in fraud detection and generative AI.
- Why this job: Join a dynamic team and shape the future of AI in insurance.
- Qualifications: Experience in Machine Learning Engineering and strong Python skills required.
The predicted salary is between 36000 - 60000 £ per year.
A leading insurer is investing heavily in data science, machine learning, and generative AI to transform how it understands risk, detects fraud, and delivers smarter decision-making across the business. They are now looking for a Machine Learning Engineer to join a growing data science function and help bring advanced models from research into robust, scalable production environments. This is a great opportunity to work on greenfield initiatives across fraud detection, intelligent automation, and generative AI use cases, while helping shape the organisation’s ML engineering capability and platform as it continues to evolve.
You’ll work closely with data scientists, data engineers, and software engineers, ensuring machine learning solutions are production-ready, maintainable, and able to deliver real business value.
What You’ll Be Doing- Designing and building production-ready machine learning systems
- Automating the end-to-end ML lifecycle, from experimentation through to deployment and monitoring
- Contributing to the development of a scalable ML platform and engineering standards
- Collaborating with data scientists to operationalise models
- Working with engineers and business stakeholders to deliver impactful AI and ML solutions
- Writing high-quality Python code following strong engineering principles
- Supporting architecture discussions and selecting the right modelling and deployment approaches
- Building solutions across both traditional machine learning and generative AI use cases
- Proven experience in Machine Learning Engineering or Data Science within a commercial environment
- Strong Python development skills and good software engineering practices
- Experience deploying machine learning models into production
- Understanding of core machine learning principles and modelling approaches
- Cloud platforms (Azure experience beneficial)
- Databricks or similar data platforms
- Containerisation and orchestration (Docker, Kubernetes or equivalent)
- CI/CD pipelines and modern development tooling
- Version control (Git) and collaborative development practices
- Experience within financial services, insurance, or other regulated environments
- Exposure to LLMs, generative AI or agentic AI solutions
- Experience supporting the ML lifecycle from experimentation to production
Machine Learning Engineer employer: Arthur Recruitment
Join a leading insurer that is at the forefront of data science and machine learning innovation, where you will have the opportunity to work on cutting-edge projects in fraud detection and generative AI. With a strong focus on employee growth, collaborative work culture, and a commitment to developing robust ML engineering capabilities, this role offers a unique chance to make a significant impact while enjoying a supportive environment that values your contributions. Located in a dynamic industry, you'll benefit from exposure to advanced technologies and the potential for career advancement within a rapidly evolving field.
StudySmarter Expert Advice🤫
We think this is how you could land Machine Learning Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. You never know who might have the inside scoop on job openings or can refer you directly.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your machine learning projects. Whether it's a GitHub repo or a personal website, having tangible examples of your work can really set you apart from the crowd.
✨Tip Number 3
Prepare for those interviews! Brush up on your technical knowledge and be ready to discuss your past projects in detail. Practising common ML engineering questions can help you feel more confident when it’s time to shine.
✨Tip Number 4
Don’t forget to apply through our website! We’ve got some fantastic opportunities waiting for you, and applying directly can sometimes give you a better chance of getting noticed by hiring managers.
We think you need these skills to ace Machine Learning Engineer
Some tips for your application 🫡
Tailor Your CV:Make sure your CV highlights your experience in machine learning engineering and Python development. We want to see how your skills align with the job description, so don’t be shy about showcasing relevant projects or achievements!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you’re passionate about machine learning and how you can contribute to our team. We love seeing enthusiasm and a clear understanding of the role.
Showcase Your Projects:If you've worked on any cool ML projects, make sure to mention them! Whether it's deploying models or working with generative AI, we want to know what you've done and how it relates to the role.
Apply Through Our Website:We encourage you to apply directly through our website. It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it’s super easy!
How to prepare for a job interview at Arthur Recruitment
✨Know Your ML Fundamentals
Brush up on core machine learning principles and modelling approaches. Be ready to discuss how you've applied these in real-world scenarios, especially in production environments. This will show your understanding of the field and your ability to contribute effectively.
✨Showcase Your Python Skills
Prepare to demonstrate your Python development skills. Bring examples of high-quality code you've written, and be ready to explain your engineering practices. This is crucial as strong coding abilities are a must for this role.
✨Familiarise Yourself with Tools and Platforms
Get comfortable with the technical environment mentioned in the job description, like Azure, Databricks, Docker, and Kubernetes. If you have experience with CI/CD pipelines and version control using Git, be prepared to discuss how you've used these tools in your previous projects.
✨Collaborate and Communicate
Since you'll be working closely with data scientists and engineers, practice articulating your thoughts clearly. Think of examples where you've successfully collaborated on projects, particularly those that involved operationalising models or delivering impactful AI solutions.
