At a Glance
- Tasks: Design and build infrastructure for autonomous AI systems, ensuring reliability and scalability.
- Company: Innovative Series B company revolutionising decision-making for field workers.
- Benefits: Competitive salary, flexible work environment, and opportunities for professional growth.
- Other info: Join a small, dynamic team with significant autonomy and influence.
- Why this job: Make a real impact by enabling cutting-edge AI technology at scale.
- Qualifications: 4+ years in production systems, strong Python skills, and deep AWS knowledge.
The predicted salary is between 80000 - 100000 £ per year.
About the Role
Our client, a Series B company automating decision-making for field workers at scale, is looking for a Senior AI Platform Engineer to own the infrastructure layer enabling autonomous agents to operate reliably at production scale. You'll design and build the systems that allow their applied AI team to ship production LLM applications without breaking things. The focus is infrastructure engineering for AI systems — not traditional MLOps or feature engineering.
You'll be responsible for:
- Multi-modal data pipelines — Ingesting video, audio, and structured data at scale; designing processing infrastructure that handles thousands of concurrent users
- Agentic orchestration on serverless AWS — Building Lambda + Step Functions infrastructure for autonomous workflows; managing state, cost, and reliability
- Observability and guardrails — Implementing monitoring that catches when autonomous agents fail; tracking decision quality, tool-use patterns, and failure modes
- Platform reliability — Designing systems with sub-100ms latency requirements that scale reliably as user volume grows
The platform team is small (3 people). You'll have significant autonomy and direct impact on product direction.
What We're Looking For
Must have:
- 4+ years building production systems (not just experiments or prototypes)
- Strong Python skills; experience designing libraries or shared services that other engineers depend on
- Deep AWS infrastructure knowledge — particularly serverless (Lambda, Step Functions, SQS, EventBridge)
- Experience designing reliable, maintainable systems for teams (not just individual contributor features)
- Hands-on experience with agentic AI systems, LLM orchestration frameworks (LangGraph, CrewAI, etc.), or similar multi-step autonomous workflows
- Platform engineer mindset — you think about schema design, API stability, backward/forward compatibility, and developer experience
Nice to have:
- Experience with multi-modal systems (video, audio, or image processing)
- AWS Bedrock, SageMaker, or similar managed AI services
- Observability frameworks for LLM systems (Langfuse, Arize, LangSmith)
- RAG pipelines, vector databases, or retrieval-augmented systems
- CI/CD automation; infrastructure-as-code (Terraform, CloudFormation)
Why This Matters
You're not building features. You're building the infrastructure that enables autonomous decision-making at scale. Every design choice affects reliability, cost, and whether the system can scale. This requires thinking like a platform engineer:
- How does data flow through the system without corruption?
- How do you evolve APIs without breaking consuming services?
- How do you catch failures before customers do?
- How do you keep costs predictable as scale grows?
About You
You've shipped production systems. You understand the difference between a feature and a platform. You care about schema design, API stability, and developer experience — not just "does it work today." You've thought about backward compatibility, breaking changes, and how to evolve systems without breaking the engineers who depend on them. You're comfortable with serverless, Python, and the AWS stack. You care about observability and knowing when things break. You're not intimidated by agentic AI systems — you understand they're just orchestration patterns, and infrastructure principles remain the same.
AI Platform Engineer in London employer: Wave Group
Join a dynamic Series B company at the forefront of automating decision-making for field workers, where your role as a Senior AI Platform Engineer will allow you to shape the infrastructure that powers autonomous agents. With a small, agile team, you'll enjoy significant autonomy and the opportunity to make a direct impact on product direction, all while working in a collaborative culture that prioritises innovation and employee growth. Located in a vibrant tech hub, this position offers not only competitive benefits but also a unique chance to be part of a transformative journey in AI technology.
StudySmarter Expert Advice🤫
We think this is how you could land AI Platform Engineer in London
✨Tip Number 1
Network like a pro! Reach out to folks in the industry, attend meetups, and connect with people on LinkedIn. 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 or GitHub repository showcasing your projects, especially those related to AI infrastructure. This gives potential employers a taste of what you can do beyond just a CV.
✨Tip Number 3
Prepare for interviews by practising common technical questions and scenarios specific to AI platform engineering. We recommend doing mock interviews with friends or using online platforms to get comfortable.
✨Tip Number 4
Apply through our website! We’re always on the lookout for talented individuals like you. Plus, applying directly can sometimes give you a better chance of getting noticed by hiring managers.
We think you need these skills to ace AI Platform Engineer in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that match the job description. Highlight your Python expertise, AWS knowledge, and any experience with production systems. We want to see how you fit into our vision!
Craft a Compelling Cover Letter:Use your cover letter to tell us why you're passionate about building infrastructure for AI systems. Share specific examples of your past work that align with the responsibilities listed in the job description. This is your chance to shine!
Showcase Your Projects:If you've worked on relevant projects, whether in a professional or personal capacity, make sure to mention them. We love seeing real-world applications of your skills, especially those involving multi-modal data or serverless architectures.
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 us you’re keen on joining our team!
How to prepare for a job interview at Wave Group
✨Know Your Infrastructure Inside Out
Make sure you brush up on your AWS knowledge, especially serverless technologies like Lambda and Step Functions. Be ready to discuss how you've designed reliable systems in the past and how you can apply that experience to the role.
✨Showcase Your Python Skills
Prepare to demonstrate your Python expertise by discussing libraries or shared services you've built. Think of specific examples where your code has been relied upon by other engineers, as this will highlight your collaborative mindset.
✨Think Like a Platform Engineer
Be prepared to talk about schema design, API stability, and backward compatibility. They want to see that you understand the bigger picture of building infrastructure, not just individual features. Bring examples of how you've tackled these challenges before.
✨Emphasise Observability and Reliability
Discuss your experience with monitoring systems and how you've implemented observability frameworks in the past. Highlight any specific tools you've used to catch failures early and ensure system reliability, as this is crucial for the role.
