At a Glance
- Tasks: Design and build cloud-native AI platform infrastructure on AWS and Databricks.
- Company: Award-winning B2B consultancy leading in enterprise AI innovation.
- Benefits: Competitive salary, flexible work options, and opportunities for professional growth.
- Other info: Join a dynamic team with a focus on long-term architectural impact in AI.
- Why this job: Make a real impact on cutting-edge generative AI products and own critical infrastructure.
- Qualifications: Experience in platform engineering, Kubernetes, and Terraform; Python skills are a plus.
The predicted salary is between 60000 - 80000 £ per year.
Join an award-winning B2B consultancy at the forefront of enterprise AI, building and owning the cloud-native platform infrastructure that powers production-grade conversational and generative AI products at scale.
This is a platform and infrastructure engineering role - not a data science or ML engineering position. You'll own the runtime, infrastructure, and operational layers that RAG pipelines, LLM orchestration, vector search, and evaluation workflows run on, across AWS and Databricks. The focus is on building scalable, observable, secure, and cost-efficient platform infrastructure that enables AI engineering teams to ship and operate AI products reliably in production.
What you’ll do:
- Design, build, and operate cloud-native AI platform infrastructure across AWS (Lambda, API Gateway, DynamoDB, S3, CloudWatch) and Databricks.
- Deploy and operate containerised services on Kubernetes using Terraform for infrastructure-as-code.
- Own and scale vector search infrastructure (OpenSearch, Algolia, AWS Bedrock Knowledge Bases) and embedding pipelines.
- Build and maintain CI/CD pipelines for inference services, retrievers, ingestion workflows, and RAG components.
- Implement observability across AI workloads using CloudWatch, MLflow, and OpenTelemetry - covering latency, throughput, cost, and system health.
- Apply secure-by-design principles including IAM, encryption, network controls, and audit logging.
- Work closely with AI engineers to translate prototypes and proof-of-concepts into production-ready, well-architected platform components.
What we’re looking for:
- Proven experience in platform, infrastructure, or software engineering roles delivering production-grade systems on AWS.
- Strong hands-on Kubernetes experience, specifically with EKS (Elastic Kubernetes Service) and ECS (Elastic Container Service) in production environments.
- Strong Terraform experience for infrastructure-as-code, provisioning and managing cloud infrastructure at scale.
- Experience operating containerised services, managing CI/CD pipelines, and owning observability and reliability.
- Familiarity with vector databases or search infrastructure (OpenSearch, Algolia) is a strong advantage.
- Python proficiency for scripting, automation, and deploying production services.
- Solid grasp of distributed systems, cloud-native architecture, microservices, and API design.
- Ownership mindset — comfortable operating autonomously across reliability, performance, cost, and security.
Why join?
You’ll own the foundational platform infrastructure behind a growing suite of generative AI products, working directly with senior AI and engineering leaders. This is a deep technical ownership role with long-term architectural impact, within an organisation investing heavily in AI at scale.
AI Platform/ DevOps Engineer in London employer: Lily Lifestyle
Join an innovative B2B consultancy that champions a collaborative and inclusive work culture, where your contributions directly shape the future of enterprise AI. With a strong focus on employee growth, you will have access to continuous learning opportunities and cutting-edge projects, all while enjoying the benefits of a supportive environment that values autonomy and technical excellence. Located in a vibrant tech hub, this role offers the unique advantage of working alongside industry leaders in a rapidly evolving field, ensuring your skills remain at the forefront of technology.
StudySmarter Expert Advice🤫
We think this is how you could land AI Platform/ DevOps Engineer in London
✨Join Local Tech Meetups
Get out there and mingle with fellow developers by joining local tech meetups. It’s a fantastic way to meet people who might be working at Lily Lifestyle or know someone who does. Plus, you can pick up some trendy tech skills and trends while you're at it!
✨Contribute to Open Source Projects
Show off your coding chops by jumping into open-source projects. Not only does this give you practical experience, but it also gets you noticed in the dev community. You'll create a killer portfolio that speaks volumes about your skills to Lily Lifestyle.
✨Tap into Online Developer Communities
Don’t underestimate the power of online developer communities like GitHub, Stack Overflow, and even Reddit. Participate in discussions, share your projects, and build your visibility. We can often find opportunities through these channels that can lead to a full-time gig at companies like Lily Lifestyle.
✨Explore Job Boards Specifically for Tech Roles
Keep your eyes peeled on job boards that focus on tech roles. Sites like TechCareers or Stack Overflow Jobs can often have listings for companies like Lily Lifestyle that might not show up on broader job sites. Make it a habit to check these regularly, and don’t hesitate to apply directly through our website!
We think you need these skills to ace AI Platform/ DevOps Engineer in London
Some tips for your application 🫡
Show off your coding skills:When applying for a software engineering role, it's super important to showcase your coding skills. Make sure your CV includes your tech stack, any relevant programming languages you’re comfortable with, and examples of projects you've worked on. If you have a GitHub profile, link it up! We love to see code in action.
Tailor your portfolio:For a full-time role, we’d expect to see some solid examples of your work in your portfolio. Make sure to include at least two or three projects that highlight your problem-solving skills and your ability to work with different technologies. Focus on the projects that are most relevant to the position at Lily Lifestyle.
Craft a killer cover letter:Your cover letter is your chance to stand out—make it personal! Explain why you want to work at Lily Lifestyle and how your skills align with the role. Show us your passion for software development. We dig enthusiastic candidates who understand the value of collaboration and continuous learning!
Be clear and concise:When it comes to writing your CV and cover letter, clarity is key. Avoid jargon that could confuse us and stick to simple, direct language. Highlight your achievements with quantifiable results where possible, and keep everything easy to read. A well-organised application goes a long way!
How to prepare for a job interview at Lily Lifestyle
✨Brush Up on Your Coding Skills
For a full-time software engineering role, it's crucial that we stay sharp with our coding abilities. Expect technical questions that might involve solving problems on the spot or discussing algorithms. Practise on platforms like LeetCode or HackerRank to get comfortable with the types of questions that often come up.
✨Know Your Tools and Frameworks
Make sure we’re well-acquainted with the tools and technologies listed in the job description. Familiarise ourselves with any specific frameworks or programming languages mentioned. If Lily Lifestyle uses React or Node.js, for instance, be ready to discuss how we’ve used them in previous projects or coursework.
✨Showcase Your Projects
Bring along a portfolio that highlights our best work. This could be code samples, GitHub repositories, or any side projects we’ve built. Make sure we can talk through our thought process for each project, especially the challenges we faced and how we solved them—this shows our problem-solving skills in action.
✨Prepare for Behavioural Questions
While technical skills are key, full-time positions also require cultural fit. Be ready to discuss our previous experiences and how we handle teamwork, conflict, and deadlines. Brush up on the STAR method—Situation, Task, Action, Result—to clearly articulate our past experiences when discussing how we've contributed to a team.
