At a Glance
- Tasks: Design and operate a Kubernetes-based MLOps platform for AI and LLM workloads.
- Company: Join Lorien, a leader in innovative tech solutions.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Be part of a dynamic team driving the future of AI technology.
- Why this job: Make a real impact by enabling AI at scale in a hands-on leadership role.
- Qualifications: Experience in building platforms and familiarity with MLOps or LLM workloads.
The predicted salary is between 60000 - 80000 £ per year.
Lorien is seeking a Lead Platform / DevOps / MLOps Engineer to design and operate a Kubernetes-based MLOps platform powering production AI and LLM workloads. This hands-on technical leadership role requires building and operating MLOps platforms on Kubernetes while supporting data scientists in real production workflows.
The ideal candidate has proven experience building usable internal platforms and exposure to MLOps or LLM workloads. This position offers an impactful role in enabling AI at scale.
MLOps Platform Engineer — AI at Scale employer: Lorien
Lorien is an exceptional employer that fosters a collaborative and innovative work culture, perfect for those passionate about advancing AI technologies. With a strong focus on employee growth, we offer ample opportunities for professional development and hands-on experience in cutting-edge MLOps practices. Located in a vibrant tech hub, our team enjoys a dynamic environment that encourages creativity and impactful contributions to the future of AI.
StudySmarter Expert Advice🤫
We think this is how you could land MLOps Platform Engineer — AI at Scale
✨Tip Number 1
Network like a pro! Reach out to folks in the MLOps community on LinkedIn or attend meetups. 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 work with Kubernetes and MLOps platforms. This could be a game-changer during interviews, as it gives potential employers a taste of what you can do.
✨Tip Number 3
Prepare for technical interviews by brushing up on your knowledge of AI and LLM workloads. We recommend doing mock interviews with friends or using online platforms to get comfortable with the format.
✨Tip Number 4
Don’t forget to apply through our website! It’s the best way to ensure your application gets seen by the right people. Plus, we love seeing candidates who are proactive about their job search!
We think you need these skills to ace MLOps Platform Engineer — AI at Scale
Some tips for your application 🫡
Show Your Technical Skills:Make sure to highlight your experience with Kubernetes and MLOps in your application. We want to see how you've built and operated platforms before, so don’t hold back on the details!
Tailor Your Application:Customise your CV and cover letter to reflect the job description. Use keywords from the posting to show us you understand what we're looking for and how you fit the bill.
Share Real Examples:When discussing your past experiences, give us concrete examples of projects you've worked on. We love to hear about your hands-on work and how it relates to supporting data scientists in production workflows.
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 this exciting role in enabling AI at scale!
How to prepare for a job interview at Lorien
✨Know Your Kubernetes Inside Out
Make sure you brush up on your Kubernetes knowledge. Be ready to discuss how you've used it in past projects, especially in relation to MLOps platforms. Prepare examples of challenges you've faced and how you overcame them.
✨Showcase Your Hands-On Experience
This role is all about practical skills, so be prepared to talk about your hands-on experience with MLOps and LLM workloads. Bring specific examples of platforms you've built or operated, and highlight any tools or technologies you've integrated.
✨Understand the Data Science Workflow
Since you'll be supporting data scientists, it's crucial to understand their workflows. Familiarise yourself with common challenges they face and think about how your platform solutions can alleviate those issues. This will show that you’re not just technically savvy but also user-focused.
✨Prepare for Technical Questions
Expect some technical grilling during the interview. Brush up on key concepts related to MLOps, CI/CD pipelines, and cloud services. Practise explaining complex ideas simply, as this will demonstrate your ability to communicate effectively with both technical and non-technical stakeholders.
