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- Tasks: Create innovative AI solutions that transform business processes and enhance products.
- Company: Join Checkout.com, a leading fintech company revolutionising digital payments.
- Benefits: Flexible hybrid work model, competitive salary, and opportunities for personal growth.
- Why this job: Be part of a dynamic team shaping the future of AI in fintech.
- Qualifications: Experience with AI technologies and strong backend engineering skills required.
- Other info: Collaborative environment with a focus on personal development and impactful work.
The predicted salary is between 36000 - 60000 £ per year.
Checkout.com is where the world checks out. Our global network powers billions of transactions every year, making money move without making a fuss. We spent years perfecting a service most people will never notice. Because when digital payments just work, businesses grow, customers stay, and no one stops to think about why. With 19 offices spanning six continents, we feel at home everywhere – but London is our HQ. Wherever our people work their magic, they’re fast-moving, performance-obsessed, and driven by being better every day. Ideal. Because a role here isn’t just another job; it’s a career-defining opportunity to build the future of fintech.
We’re looking for an AI Engineer who brings fresh ideas, has a curious nature and is passionate about working in an Engineering environment which focuses on building products to achieve the company's strategic goals. As a Software Engineer within Checkout.com’s AI Centre of Excellence, you will rapidly build and iterate on Generative AI solutions to transform how cross-functional teams operate. You will work with teams from across Checkout.com’s portfolio of products and services who all have a real customer first philosophy. You will design, build, and deploy AI solutions that enhance products and streamline business processes in collaboration with cross-functional teams. You will also be an avid user of coding agents and know how to get the most out of them.
How you’ll make an impact:
- Partner with product managers, non-technical business users and engineers to research, validate, and deliver high-impact Generative AI use cases
- Design and build user facing products that leverage AI capabilities for optimising internal workflows with a focus on adoption and user satisfaction
- Build, deploy, measure and iterate on LLM-based applications (e.g., agents, knowledge extraction, automation solutions).
- Develop end-to-end AI solutions on cloud platforms (AWS, GCP, or Azure) taking a build-it-yourself attitude to the entire development lifecycle including test suites, deployment pipelines (CICD), agent evaluations and observability
- Design systems that continuously optimise performance, reliability, and scalability of AI systems
What we’re looking for:
- Educational Background: Bachelor's or Master's degree in Computer Science, Engineering, or related field
- AI & LLM Expertise: Hands-on experience with LLMs (e.g. GPT, Gemini, Claude Code) and applying them to real-world problems
- Backend Proficiency: Strong backend engineering expertise, including RESTful APIs and micro-services design
- Technical Stack: Proficiency in Python/JavaScript/Typescript and familiarity with AI frameworks (e.g., LangChain, PydanticAI and Google Agent Development Kit)
- Cloud & Infrastructure: Solid understanding of cloud computing (AWS, GCP, or Azure), containerisation (Docker, Kubernetes), and Infrastructure-as-Code (Terraform)
- Soft Skills: Proven problem-solving skills and ability to thrive in fast-paced environments, with excellent communication and collaboration skills across diverse teams
Preferred qualifications:
- Proficiency in Python
- Frontend development experience with JavaScript/TypeScript and frameworks such as React
- Experience developing Generative AI applications, including model fine-tuning and prototyping
- Awareness of ethical AI practices and emerging standards in AI governance
- Track record of rapid prototyping with measurable ROI
- Contributions to open-source projects or a strong GitHub portfolio
- High technical curiosity and eagerness to learn new platforms
Bring all of you to work
We create the conditions for high performers to thrive – through real ownership, fewer blockers, and work that makes a difference from day one. Here, you’ll move fast, take on meaningful challenges, and be recognized for the impact you deliver. It’s a place where ambition gets met with opportunity – and where your growth is in your hands. We work as one team, and we back each other to succeed. So whatever your background or identity, if you’re ready to grow and make a difference, you’ll be right at home here. It’s important we set you up for success and make our process as accessible as possible. So let us know in your application, or tell your recruiter directly, if you need anything to make your experience or working environment more comfortable.
Life at Checkout.com
We understand that work is just one part of your life. Our hybrid working model offers flexibility, with three days per week in the office to support collaboration and connection.
AI Engineer London employer: Checkout Ltd
Contact Detail:
Checkout Ltd Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer London
✨Tip Number 1
Network like a pro! Reach out to people in the industry, attend meetups, and connect with current employees at Checkout.com. A friendly chat can sometimes lead to opportunities that aren’t even advertised.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your AI projects, especially those involving LLMs or Generative AI. This will give you an edge and demonstrate your hands-on experience to potential employers.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and soft skills. Practice coding challenges and be ready to discuss how you've tackled real-world problems using AI solutions.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets noticed. Plus, it shows you’re genuinely interested in being part of the Checkout.com team.
We think you need these skills to ace AI Engineer London
Some tips for your application 🫡
Show Your Passion: When you're writing your application, let your enthusiasm for AI and engineering shine through. We want to see that you're not just ticking boxes but genuinely excited about the role and how you can contribute to our mission.
Tailor Your CV: Make sure your CV is tailored to the job description. Highlight your experience with LLMs, backend engineering, and any relevant projects. We love seeing how your skills align with what we're looking for!
Be Clear and Concise: Keep your application clear and to the point. Use bullet points where possible to make it easy for us to read. We appreciate a well-structured application that gets straight to the good stuff!
Apply Through Our Website: Don’t forget to apply through our website! It’s the best way for us to receive your application and ensures you’re considered for the role. Plus, it shows you’re serious about joining our team!
How to prepare for a job interview at Checkout Ltd
✨Know Your AI Stuff
Make sure you brush up on your knowledge of LLMs and generative AI solutions. Be ready to discuss how you've applied these technologies in real-world scenarios, as this will show your hands-on experience and technical expertise.
✨Showcase Your Problem-Solving Skills
Prepare examples that highlight your problem-solving abilities, especially in fast-paced environments. Think about specific challenges you've faced and how you tackled them, particularly in engineering contexts.
✨Understand the Company Culture
Familiarise yourself with Checkout.com’s values and work culture. They emphasise collaboration and a customer-first philosophy, so be ready to discuss how you can contribute to their mission and work effectively within cross-functional teams.
✨Ask Insightful Questions
Prepare thoughtful questions about the role and the company. This shows your genuine interest and curiosity. You might ask about their approach to ethical AI practices or how they measure the success of AI solutions, which aligns with their focus on high-impact use cases.