At a Glance
- Tasks: Design and build infrastructure for autonomous AI systems at production scale.
- Company: Innovative Series B company revolutionising decision-making for field workers.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Dynamic environment focused on innovation and career advancement.
- Why this job: Join a cutting-edge team and shape the future of AI technology.
- Qualifications: 4+ years in production systems, strong Python skills, and AWS expertise.
The predicted salary is between 60000 - 80000 £ per year.
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.
Key Responsibilities:
- Multi-modal data pipelines — Ingesting video, audio, and structured data at scale.
- Agentic orchestration on serverless AWS — Building Lambda + Step Functions infrastructure for autonomous workflows.
- Observability and guardrails — Implementing monitoring that catches when autonomous agents fail; tracking decision quality, tool-use patterns, and failure modes.
Qualifications:
- 4+ years building production systems (not just experiments or prototypes).
- Strong Python skills.
- Deep AWS infrastructure knowledge — particularly serverless (Lambda, Step Functions, SQS, EventBridge).
- Hands-on experience with agentic AI systems, LLM orchestration frameworks (LangGraph, CrewAI, etc.).
- Platform engineer mindset — you think about schema design, API stability, backward/forward compatibility, and developer experience.
- Experience with multi-modal systems (video, audio, or image processing).
- AWS Bedrock, SageMaker, or similar managed AI services.
- RAG pipelines, vector databases, or retrieval-augmented systems.
You're not building features. You're building the infrastructure that enables autonomous decision-making at scale. This requires thinking like a platform engineer: How does data flow through the system without corruption? You've shipped production systems. You're comfortable with serverless, Python, and the AWS stack. You're not intimidated by agentic AI systems — you understand they're just orchestration patterns, and infrastructure principles remain the same.
Engineering Support in London employer: Wave Group
Join a forward-thinking Series B company that is revolutionising decision-making for field workers through cutting-edge AI technology. As an Engineering Support professional, you'll thrive in a dynamic work culture that prioritises innovation and collaboration, while enjoying ample opportunities for personal and professional growth. With a focus on building robust infrastructure for autonomous systems, you'll be part of a team that values your expertise and encourages you to push the boundaries of what's possible in AI.
StudySmarter Expert Advice🤫
We think this is how you could land Engineering Support in London
✨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 get your foot in the door.
✨Tip Number 2
Show off your skills! Create a portfolio showcasing your projects, especially those related to infrastructure engineering and AI systems. We want to see what you can do, so make it easy for us to find your best work.
✨Tip Number 3
Prepare for interviews by brushing up on your technical knowledge and problem-solving skills. We recommend practicing common interview questions related to AWS, Python, and multi-modal systems to really impress us.
✨Tip Number 4
Apply through our website! It’s the best way to ensure your application gets seen. Plus, we love seeing candidates who take the initiative to connect directly with us.
We think you need these skills to ace Engineering Support in London
Some tips for your application 🫡
Tailor Your CV:Make sure your CV reflects the skills and experiences that align with the job description. Highlight your experience with AWS, Python, and any relevant infrastructure projects you've worked on. We want to see how you can contribute to our mission!
Craft a Compelling Cover Letter:Your cover letter is your chance to shine! Use it to explain why you're passionate about building infrastructure for AI systems. Share specific examples of your past work that demonstrate your platform engineering mindset and experience with multi-modal data.
Showcase Your Projects:If you've built production systems or worked on relevant projects, don’t hold back! Include links or descriptions of your work that showcase your hands-on experience with serverless architectures and agentic AI systems. We love seeing what you've accomplished!
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 at StudySmarter!
How to prepare for a job interview at Wave Group
✨Know Your Infrastructure Inside Out
Make sure you’re well-versed in the specific AWS services mentioned in the job description, like Lambda and Step Functions. Brush up on how these tools work together to support autonomous workflows, as this will show your potential employer that you can hit the ground running.
✨Showcase Your Production Experience
Prepare examples from your past work where you've built production systems, not just prototypes. Be ready to discuss challenges you faced and how you overcame them, especially in relation to multi-modal data pipelines and observability.
✨Think Like a Platform Engineer
During the interview, demonstrate your understanding of schema design, API stability, and backward/forward compatibility. Use real-world scenarios to explain how you ensure data integrity and flow through systems, which is crucial for the role.
✨Familiarise Yourself with Agentic AI Systems
Since the role involves working with agentic AI systems, make sure you understand orchestration patterns and how they apply to infrastructure engineering. Discuss any hands-on experience you have with frameworks like LangGraph or CrewAI to highlight your relevant skills.
