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- Tasks: Design and operate robust Kubernetes-based platforms for AI and machine learning.
- Company: Join a forward-thinking tech company focused on AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Collaborative environment with a focus on cutting-edge technology and career advancement.
- Why this job: Make a real impact in the exciting world of AI and ML infrastructure.
- Qualifications: Strong background in platform engineering and hands-on Kubernetes experience.
The predicted salary is between 70000 - 90000 £ per year.
Build and operate the platforms that make AI and machine learning work at scale.
We're looking for a Senior Platform Engineer to join our team and play a key role in designing and operating the platform that underpins AI and machine learning delivery. This is a hands-on senior platform role, focused on building robust, Kubernetes-based platforms that enable MLOps engineers, ML engineers, and data scientists to deploy, run, and manage models safely and effectively in production.
While you'll need a strong understanding of how machine learning and LLM workloads are trained, packaged, deployed, and served, this is not a "deploy models all day" role. You'll be responsible for building a production-grade AI / ML platform, not just running clusters.
- Design, build, and operate a Kubernetes-based platform that supports multiple ML and engineering teams.
- Model packaging, deployment, and promotion.
- Build shared platform services that enable consistent, repeatable model deployment, even where day-to-day deployment is owned by MLOps or ML engineers.
- Work closely with data scientists and MLOps engineers to ensure the platform is genuinely usable and fit for purpose.
- Contribute to architectural decisions while remaining hands-on with implementation.
This role is ideal for someone who sees themselves first and foremost as a platform engineer, with the depth to support AI and ML workloads properly.
- Strong background as a Senior Platform Engineer or Senior DevOps Engineer.
- Deep, hands-on experience building and operating Kubernetes-based platforms.
- Proven experience building internal platforms for other engineers, not just running workloads.
- Strong grasp of operational fundamentals: monitoring, logging, reliability, incidents, and maintainability.
- Comfortable collaborating closely with MLOps engineers and data scientists, even where responsibilities differ.
- ML platform & MLOps knowledge (important).
You don't need to be a full-time MLOps engineer - but you do need practical understanding of how ML and AI workloads behave in production.
- Supporting LLM-based workloads, including performance and scaling considerations.
- Awareness of emerging tooling around Responsible / Trustworthy AI or comparable solutions.
This ensures you're building a platform that actually works for AI use cases - not a generic compute layer. Working in organisations with a clear AI or data platform strategy. Supporting data scientists or ML engineers at scale.
If you enjoy solving hard platform problems and understand that AI places real, specific demands on infrastructure, this role gives you the space and responsibility to make a genuine impact.
Senior AWS Platform Engineer in London employer: Lorien
Contact Detail:
Lorien Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior AWS Platform Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that Senior AWS Platform Engineer role.
✨Tip Number 2
Show off your skills! Create a portfolio or GitHub repo showcasing your Kubernetes projects and any AI/ML platforms you've built. This gives potential employers a taste of what you can do and how you think.
✨Tip Number 3
Prepare for those interviews! Brush up on your knowledge of MLOps and Kubernetes. We recommend practising common interview questions and even doing mock interviews with friends to boost your confidence.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals like you. Plus, applying directly can sometimes give you an edge over other candidates.
We think you need these skills to ace Senior AWS Platform Engineer in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the Senior Platform Engineer role. Highlight your hands-on experience with Kubernetes and any relevant AI/ML projects you've worked on. We want to see how you can contribute to our platform!
Craft a Compelling Cover Letter: Your cover letter is your chance to shine! Use it to explain why you're passionate about building robust platforms for AI and ML. Share specific examples of your past work that demonstrate your ability to design and operate effective solutions. We love a good story!
Showcase Collaboration Skills: Since this role involves working closely with MLOps engineers and data scientists, make sure to highlight your collaboration skills in your application. Talk about how you've successfully partnered with other teams in the past to deliver impactful results. We value teamwork!
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 shows you’re keen on joining our team at StudySmarter!
How to prepare for a job interview at Lorien
✨Know Your Kubernetes Inside Out
Make sure you brush up on your Kubernetes knowledge before the interview. Be ready to discuss how you've built and operated Kubernetes-based platforms in the past, and think of specific examples where you've tackled challenges related to scaling and reliability.
✨Understand AI and ML Workloads
Familiarise yourself with how AI and machine learning workloads are packaged, deployed, and served. Be prepared to explain your understanding of MLOps and how it integrates with platform engineering. This will show that you’re not just a techie but someone who understands the bigger picture.
✨Collaborate Like a Pro
Since this role involves working closely with data scientists and MLOps engineers, think of examples where you've successfully collaborated across teams. Highlight your communication skills and how you’ve ensured that platforms are user-friendly and fit for purpose.
✨Showcase Your Problem-Solving Skills
Be ready to discuss specific platform problems you've solved in the past, especially those related to AI infrastructure. Think about the operational fundamentals like monitoring and logging, and how you've implemented solutions that enhance maintainability and reliability.