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- Tasks: Design and maintain a scalable ML serving platform while developing cloud infrastructure.
- Company: Join a fast-growing, AI-first company transforming visual content creation for millions.
- Benefits: Enjoy hybrid work, competitive salary, and a vibrant office in Central London.
- Why this job: Work on real-world AI challenges with passionate colleagues in a supportive environment.
- Qualifications: 5+ years in SaaS systems, experience with Kubernetes, and familiarity with CI/CD tools required.
- Other info: Participate in an on-call rotation with extra pay for weekends.
The predicted salary is between 60000 - 84000 £ per year.
A fast-growing, AI-first company is transforming how people create visual content—powering popular apps used by millions, and helping creators and brands grow through cutting-edge technology. We are looking for an experienced Infrastructure / ML Platform Engineer to join our Machine Learning Platform team. This team builds and supports the platform that powers advanced AI models, helping bring research into production at scale.
📍 Hybrid role – 3 days onsite in Central London
What you’ll do:
- Design, build, and maintain a scalable and reliable ML serving platform
- Develop cloud infrastructure and internal tools to support research and engineering teams
- Set up and manage CI/CD pipelines and monitoring systems
- Build self-serve tools to simplify deployment and development
- Share best practices across teams and help level up the platform
- Take part in an on-call rotation (weekends included, with extra pay)
What we’re looking for:
- 5+ years of experience running scalable SaaS systems in GCP or AWS, or Azure
- 3+ years with Kubernetes, Helm/Kustomize, and tools like Terraform or Pulumi
- Experience with microservices, containerized environments, and GitOps (e.g. ArgoCD)
- Familiarity with CI/CD tools like GitHub Actions, Jenkins, or CircleCI
- Hands-on with monitoring tools like Prometheus and Grafana
Nice to have:
- Experience building Developer Experience (DevX) tools and workflows
- Familiarity with GPU setups (CUDA, TensorFlow, etc.)
- Strong networking and network security knowledge
- Linux/Unix skills and shell scripting
- A degree in Computer Science or a related field
What we offer:
- Hybrid work – 3 days onsite in a vibrant Central London office
- Three-stage interview process – straightforward and transparent
- Competitive salary and benefits
- Work on real-world AI challenges with smart, passionate people
Senior DevOps Engineer employer: Velocity Tech
Contact Detail:
Velocity Tech Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Senior DevOps Engineer
✨Tip Number 1
Familiarise yourself with the specific cloud platforms mentioned in the job description, such as GCP, AWS, or Azure. Having hands-on experience and being able to discuss your projects using these platforms will show that you're ready to hit the ground running.
✨Tip Number 2
Brush up on your Kubernetes and CI/CD skills, particularly with tools like Helm/Kustomize and GitHub Actions. Being able to demonstrate your knowledge of these technologies during discussions can set you apart from other candidates.
✨Tip Number 3
Prepare to discuss your experience with monitoring tools like Prometheus and Grafana. Sharing specific examples of how you've implemented these tools in past roles can highlight your practical expertise and problem-solving abilities.
✨Tip Number 4
Show enthusiasm for AI and machine learning by staying updated on the latest trends and technologies in the field. This passion can be a great conversation starter and demonstrates your commitment to contributing to the company's mission.
We think you need these skills to ace Senior DevOps Engineer
Some tips for your application 🫡
Tailor Your CV: Make sure your CV highlights relevant experience in running scalable SaaS systems, particularly in GCP, AWS, or Azure. Emphasise your expertise with Kubernetes, CI/CD tools, and any hands-on experience with monitoring tools like Prometheus and Grafana.
Craft a Compelling Cover Letter: In your cover letter, express your passion for AI and how your background aligns with the company's mission. Mention specific projects where you've designed or maintained ML platforms, and how you can contribute to their team.
Showcase Relevant Projects: If applicable, include links to GitHub repositories or personal projects that demonstrate your skills in building cloud infrastructure, CI/CD pipelines, or any DevX tools you've developed. This will give them a practical insight into your capabilities.
Prepare for Technical Questions: Anticipate technical questions related to your experience with microservices, containerised environments, and network security. Be ready to discuss your problem-solving approach and share examples from your past work that showcase your expertise.
How to prepare for a job interview at Velocity Tech
✨Showcase Your Technical Skills
Be prepared to discuss your experience with cloud platforms like GCP, AWS, or Azure. Highlight specific projects where you've implemented scalable SaaS systems and how you utilised tools like Kubernetes, Terraform, or Pulumi.
✨Demonstrate Problem-Solving Abilities
Expect scenario-based questions that assess your ability to troubleshoot and optimise ML serving platforms. Share examples of challenges you've faced in previous roles and how you overcame them.
✨Familiarise Yourself with CI/CD Practices
Since the role involves setting up CI/CD pipelines, be ready to discuss your experience with tools like GitHub Actions or Jenkins. Prepare to explain how you've streamlined deployment processes in past projects.
✨Emphasise Collaboration and Best Practices
The company values sharing best practices across teams. Be ready to talk about how you've collaborated with cross-functional teams and contributed to improving workflows or developer experience in your previous roles.
