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- Tasks: Design and maintain a cutting-edge ML serving platform while supporting research and engineering teams.
- 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 scalable SaaS systems; experience with Kubernetes, CI/CD tools, and monitoring.
- Other info: Participate in an on-call rotation with extra pay and a straightforward interview process.
The predicted salary is between 54000 - 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, 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 set you apart during interviews.
✨Tip Number 2
Brush up on your knowledge of Kubernetes and CI/CD tools like GitHub Actions or Jenkins. Be prepared to share examples of how you've implemented these technologies in past roles, as practical experience is highly valued.
✨Tip Number 3
Showcase your understanding of monitoring tools like Prometheus and Grafana. Discuss any relevant projects where you’ve used these tools to enhance system reliability and performance, as this aligns closely with the role's responsibilities.
✨Tip Number 4
Network with current employees or professionals in the field through platforms like LinkedIn. Engaging with them can provide insights into the company culture and expectations, which can be beneficial during your interview process.
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 self-serve tools. This will provide tangible evidence of your capabilities.
Prepare for Technical Questions: Anticipate technical questions related to your experience with microservices, containerised environments, and networking. Be ready to discuss your problem-solving approach and share examples from your past work.
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 be ready to dive into the technical details of your work with Kubernetes, Terraform, and CI/CD tools.
✨Demonstrate Problem-Solving Abilities
Expect scenario-based questions that assess your problem-solving skills. Think about challenges you've faced in previous roles, particularly around building and maintaining ML serving platforms, and how you overcame them.
✨Emphasise Collaboration and Best Practices
This role involves sharing best practices across teams. Be ready to discuss how you've collaborated with others in the past, particularly in developing internal tools or simplifying deployment processes, and how you can contribute to a culture of knowledge sharing.
✨Prepare for On-Call Scenarios
Since the position includes an on-call rotation, be prepared to talk about your experience with incident management and how you handle high-pressure situations. Share examples of how you've effectively managed outages or critical incidents in the past.
