At a Glance
- Tasks: Lead the design and operation of an MLOps platform for AI and machine learning.
- Company: Join a forward-thinking tech company in London with a focus on innovation.
- Benefits: Competitive salary, flexible working, and opportunities for professional growth.
- Other info: Dynamic role with significant challenges and opportunities for career advancement.
- Why this job: Make a real impact by enabling cutting-edge AI workloads in production environments.
- Qualifications: Strong background in platform engineering and hands-on Kubernetes experience required.
The predicted salary is between 80000 - 100000 £ per year.
Building the platforms that make AI and machine learning work in production. We're looking for a Lead Platform Engineer to join a growing engineering organisation and play a pivotal role in designing, building, and operating an MLOps platform that enables AI and data science teams to deliver reliably in production. This is a senior, hands-on technical leadership role, not a people-management position. You'll lead through technical depth, judgement, and delivery, building the tooling, workflows, and operational foundations that allow data scientists and ML engineers to experiment, deploy, and run ML and LLM-based workloads safely and at scale. The focus is not simply on running Kubernetes clusters – it's on layering real MLOps capability on top of Kubernetes to create a platform that is usable, supportable, and trusted in live environments.
What you’ll be doing
- Provide technical leadership across platform, DevOps, and MLOps activities
- Design, build, and operate a Kubernetes-based MLOps platform supporting the full model lifecycle
- Implement and run MLOps tooling that enables teams to:
- Experiment with models and notebooks
- Package, version, and deploy models
- Run scalable inference and LLM-based workloads
- Build and operate model serving and inference platforms within Kubernetes environments
- Work closely with data scientists and ML engineers to ensure the platform is usable, well-documented, and aligned to real workflows
- Own platform operability, reliability, security, and supportability in production
- Troubleshoot complex issues across Kubernetes, platform services, and MLOps layers
- Contribute to architectural decisions while staying hands-on with delivery
- Apply pragmatic engineering judgement in environments where AI workloads place real operational demands on infrastructure
What we’re looking for
This role suits someone who is fundamentally a strong platform engineer, with the depth to apply those skills confidently to MLOps.
Essential experience
- Strong background as a Senior or Lead Platform Engineer / DevOps Engineer
- Deep, hands-on experience building and operating Kubernetes-based platforms
- Strong practical experience with Helm and Infrastructure as Code (e.g. Terraform)
- Proven experience extending Kubernetes with higher-level platforms and services, not treating it as the finished product
- Strong understanding of operational fundamentals: monitoring, logging, incident response, reliability, and maintenance
- Comfortable working directly with engineers and data scientists to support real production workloads
- MLOps experience (key to the role)
- You’ll work deeply 'in the weeds' of MLOps platforms, enabling ML and LLM workloads (not model research).
- Experience in areas such as:
- Building or operating MLOps platforms using tools like Kubeflow or similar frameworks
- Running model serving and inference platforms (e.g. KServe, vLLM, or equivalent)
- Supporting LLM-based workloads, including optimisation and serving considerations
- Providing notebook-based environments such as JupyterHub in secure platforms
- Exposure to emerging tooling such as InstructLab, Trustworthy / Responsible AI tooling, or comparable solutions
Desirable experience
- Building internal platforms specifically for data science and ML teams
- Operating AI-enabled or data-driven systems in production
- Experience in regulated, security-conscious, or high-assurance environments
- Designing platforms that balance user flexibility with governance and control
If you believe Kubernetes is the base, not the product, enjoy operating close to the metal, and like solving hard platform problems that enable others to succeed, this role offers real challenge and impact. If interested, apply now!
Lead Platform Engineer in London employer: Energy Jobline ZR
As a Lead Platform Engineer in London, you will join a dynamic engineering organisation that prioritises innovation and collaboration. Our work culture fosters technical leadership and hands-on problem-solving, providing ample opportunities for professional growth in the rapidly evolving fields of AI and MLOps. With a focus on building reliable platforms that support cutting-edge machine learning workloads, we offer a unique environment where your contributions directly impact the success of our data science teams.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Platform Engineer in London
✨Tip Number 1
Network like a pro! Reach out to your connections in the tech and MLOps space. Attend meetups, webinars, or even local tech events in London. 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. Include any projects where you've built or operated these systems. This will give potential employers a clear view of what you can bring to the table.
✨Tip Number 3
Prepare for technical interviews by brushing up on your hands-on skills. Be ready to discuss your experience with tools like Helm and Terraform, and how you've tackled real-world challenges in MLOps. Practice coding problems related to platform engineering to stay sharp!
✨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, it shows you're genuinely interested in joining our team and contributing to our mission in the AI and MLOps space.
We think you need these skills to ace Lead Platform Engineer in London
Some tips for your application 🫡
Show Your Technical Skills:Make sure to highlight your hands-on experience with Kubernetes and MLOps in your application. We want to see how you've tackled real-world problems and built platforms that support AI workloads.
Tailor Your Application:Don’t just send a generic CV! Tailor your application to reflect the specific skills and experiences mentioned in the job description. We love seeing candidates who take the time to connect their background to what we’re looking for.
Be Clear and Concise:When writing your application, keep it clear and to the point. We appreciate well-structured applications that make it easy for us to see your qualifications and fit for 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 shows you’re keen on joining our team!
How to prepare for a job interview at Energy Jobline ZR
✨Know Your Kubernetes Inside Out
Make sure you can talk confidently about your hands-on experience with Kubernetes. Be prepared to discuss specific projects where you've built or operated Kubernetes-based platforms, and how you've extended its capabilities with tools like Helm and Terraform.
✨Showcase Your MLOps Expertise
Highlight your experience with MLOps platforms and tools such as Kubeflow or KServe. Be ready to explain how you've supported ML workloads in production, focusing on the practical challenges you've faced and how you overcame them.
✨Demonstrate Technical Leadership
Since this role is about technical depth rather than people management, prepare examples that showcase your ability to lead through technical judgement. Discuss how you've contributed to architectural decisions and delivered complex projects while working closely with engineers and data scientists.
✨Prepare for Problem-Solving Scenarios
Expect to be asked about troubleshooting complex issues across Kubernetes and MLOps layers. Think of specific incidents you've resolved, detailing your approach to incident response, monitoring, and ensuring reliability in production environments.
