Find a job 
Featured Companies 
Featured Universities 
For Companies At a Glance
- Tasks: Design and manage cutting-edge AI infrastructure for top global enterprises.
- Company: Fast-growing Series A deep tech company revolutionising AI automation.
- Benefits: Competitive salary, remote work options, and a culture of innovation.
- Why this job: Join a team at the forefront of AI infrastructure and make a real impact.
- Qualifications: Experience in cloud infrastructure, Kubernetes, and client-facing roles.
- Other info: Collaborate with world-class engineers in a high-performance environment.
The predicted salary is between 36000 - 60000 £ per year.
Our client is a pioneering deep tech company transforming how enterprises leverage artificial intelligence to automate complex analytics workflows. Following a successful Series A funding round, the company is scaling rapidly to meet growing demand from some of the world's largest organizations.
Their platform delivers AI-powered digital workers — autonomous systems that perform sophisticated analytics and data science tasks continuously and intelligently. By replacing fragmented tools, manual consulting, and costly licenses, these digital workers enable enterprises to achieve insights 10x faster and at a fraction of the cost, with guaranteed outcomes and zero operational overhead.
As an Infrastructure AI Engineer, you'll design, deploy, and manage the infrastructure that powers some of the most advanced AI systems in production today. You'll work at the intersection of cloud computing, AI operations, and DevOps, ensuring scalable, secure, and high-performing environments for enterprise AI deployments.
This is a hands-on, client-facing role where you'll collaborate with technical and business teams to bring cutting-edge AI agents and workflows to life across multi-cloud and on-premise environments.
What You'll Do
- Architect, deploy, and maintain production-grade AI solutions across multi-cloud and on-prem infrastructures.
- Design and manage Kubernetes-based environments to support high-performance AI workloads.
- Implement best practices for availability, observability, scalability, and security in AI infrastructure.
- Collaborate with enterprise clients to understand deployment constraints and design tailored solutions.
- Integrate and operationalize LLMs, RAGs, MCPs, and agentic AI workflows into robust environments.
- Build and optimize CI/CD pipelines and infrastructure-as-code (IaC) frameworks (e.g., Terraform, Helm).
- Partner closely with AI engineering teams to ensure smooth delivery from prototype to production.
What You'll Bring
- Strong background in cloud infrastructure engineering — experience with at least one major provider (AWS, Azure, or GCP).
- Proven ability to deploy and manage systems across hybrid or multi-cloud and on-premise environments.
- Expertise with Kubernetes, Docker, and container orchestration at scale.
- Familiarity with DevOps and site reliability engineering (SRE) best practices.
- Experience with CI/CD automation and infrastructure-as-code tools.
- Understanding of AI/ML deployment patterns and how to support model-driven workloads in production.
- Strong client-facing communication and problem-solving skills, with a forward-deployed engineering mindset.
Why Join
- Join a fast-growing Series A company at the forefront of AI infrastructure innovation.
- Work on cutting-edge, real-world AI deployments for top global enterprises.
- Collaborate with world-class engineers and AI practitioners in a high-performance culture.
- Be part of a team that values ownership, excellence, and impact — where winning together is the goal.
AI Engineer (Infrastructure) in London employer: Space Executive
Contact Detail:
Space Executive Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land AI Engineer (Infrastructure) in London
✨Network Like a Pro
Get out there and connect with people in the AI and tech space! Attend meetups, webinars, or industry conferences. You never know who might be looking for someone just like you!
✨Show Off Your Skills
Create a portfolio showcasing your projects and achievements. Whether it's GitHub repos or case studies, let your work speak for itself. This is your chance to shine and show potential employers what you can do!
✨Ace the Interview
Prepare for technical interviews by brushing up on your cloud infrastructure knowledge and Kubernetes skills. Practice common interview questions and scenarios, and don’t forget to ask insightful questions about the company’s AI projects!
✨Apply Through Our Website
Don’t forget to apply directly through our website! It’s the best way to ensure your application gets noticed. Plus, we love seeing candidates who are proactive and eager to join our team!
We think you need these skills to ace AI Engineer (Infrastructure) in London
Some tips for your application 🫡
Tailor Your CV: Make sure your CV reflects the skills and experiences that align with the AI Engineer role. Highlight your cloud infrastructure experience and any hands-on projects you've worked on that relate to AI systems.
Craft a Compelling Cover Letter: Use your cover letter to tell us why you're passionate about AI and how your background makes you a great fit for our team. Be specific about your experience with Kubernetes, Docker, and CI/CD tools.
Showcase Your Problem-Solving Skills: In your application, give examples of how you've tackled challenges in previous roles. We love seeing how you approach problems, especially in client-facing situations.
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 as quickly as possible!
How to prepare for a job interview at Space Executive
✨Know Your Tech Inside Out
Make sure you’re well-versed in cloud infrastructure engineering, especially with major providers like AWS, Azure, or GCP. Brush up on your Kubernetes and Docker skills, as these will likely come up during technical discussions.
✨Showcase Your Problem-Solving Skills
Prepare to discuss specific challenges you've faced in previous roles and how you tackled them. This role is client-facing, so demonstrating your ability to communicate effectively and solve problems will be key.
✨Familiarise Yourself with AI Workflows
Understand the deployment patterns of AI/ML models and be ready to talk about how you’ve supported model-driven workloads in production. This knowledge will show that you’re not just technically savvy but also understand the broader context of your work.
✨Prepare Questions for Them
Interviews are a two-way street! Prepare insightful questions about their AI infrastructure, team dynamics, and future projects. This shows your genuine interest in the role and helps you assess if it’s the right fit for you.