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 70000 - 90000 £ 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 employer: Wave Group
Join a dynamic Series B company at the forefront of AI innovation, where you'll play a pivotal role in shaping the infrastructure that empowers autonomous decision-making for field workers. With a small, agile team, you'll enjoy significant autonomy and the opportunity to make a direct impact on product direction while working in a collaborative environment that values your expertise and encourages professional growth. Located in a vibrant tech hub, this role offers not only competitive benefits but also a culture that fosters creativity and innovation, making it an ideal place for passionate engineers looking to advance their careers.
StudySmarter Expert Advice🤫
We think this is how you could land AI Platform Engineer
✨Tip Number 1
Network like a pro! Reach out to folks in the industry on LinkedIn or at meetups. We all know that sometimes it’s not just what you know, but who you know that can help you land that dream job.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to AI infrastructure. We want to see how you’ve tackled real-world problems and built systems that scale.
✨Tip Number 3
Prepare for technical interviews by brushing up on your Python and AWS knowledge. We recommend doing mock interviews with friends or using platforms that simulate the interview experience to get comfortable.
✨Tip Number 4
Don’t forget to apply through our website! We’re always on the lookout for talented individuals who are passionate about building robust AI platforms. Your next big opportunity could be just a click away!
We think you need these skills to ace AI Platform Engineer
Some tips for your application 🫡
Tailor Your Application:Make sure to customise your CV and cover letter for the AI Platform Engineer role. Highlight your experience with production systems, AWS, and Python. We want to see how your skills align with our needs!
Showcase Your Projects:Include specific examples of projects where you've built reliable infrastructure or worked with agentic AI systems. We love seeing real-world applications of your skills, so don’t hold back!
Be Clear and Concise:When writing your application, keep it straightforward. Use clear language and avoid jargon unless it's relevant. We appreciate a well-structured application that gets straight to the point.
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’s super easy!
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 systems that handle high concurrency and low latency, as this will show your understanding of the infrastructure layer.
✨Showcase Your Python Skills
Prepare to demonstrate your Python expertise by discussing libraries or shared services you've built. Bring examples of how your code has been used by other engineers, as this highlights your ability to create reliable and maintainable systems.
✨Think Like a Platform Engineer
Be prepared to talk about schema design, API stability, and backward compatibility. Share your thoughts on how to evolve systems without breaking existing features, as this will show that you understand the bigger picture of platform engineering.
✨Discuss Observability and Reliability
Have examples ready of how you've implemented monitoring and observability in past projects. Talk about how you catch failures before they impact users and ensure system reliability, as this is crucial for the role.
