Senior Cloud Platform Engineer

Some key info for you about Liberis:

We were founded in 2007

We have provided over $3bn of funding to small businesses so far

We have been named inCNBC & Statista Top 150 UK Fintechs for 2025

Were a global team with a dynamic presence in6key locations around the world

Were a thriving community of over290innovative minds

Were a vibrant melting pot celebrating over27nationalities in our team

Our team brings experience from over740previous companies from startups to global giants

We have just been named as one ofFinTechs Finest 50by Welcome to the Jungle

Were proud to be an accreditedReal Living Wageemployer ensuring everyone is paid fairly for the great work they do!

Our Product & Engineering Team:

Liberis is building the embedded finance platform that lets partners around the world offer innovative funding products to their small business customers. Were a growth-stage fintech with teams in London Nottingham Atlanta Stockholm Munich and Mumbai and were building a global Product Data & Engineering team that thrives on autonomy ownership and is focused on impact! Our teams solve real-world problems for small businesses shaping products that unlock opportunity at scale.

Engineering is going through an AI-first transformation rethinking how teams are structured and how they ship. Its changing what a small team can do! We empower our teams to make decisions move fast and take full responsibility for the solutions they deliver. Youll join a team where curiosity is encouraged and collaboration across Product Data Delivery and Engineering is the norm.

The role:

The Cloud Platform team builds and operates the shared infrastructure and developer tooling that every engineering team at Liberis depends on. Were now running all new services in GCP and are actively migrating onto GCP from our legacy Azure platform. As we scale our use of AI were looking to add a Senior Cloud Platform Engineer with an emphasis on AI Enablement to help build and run the shared infrastructure our product teams depend on and to expand how we use AI across the platform.

Our services are built and Python primarily on both GCP and Azure. Youll work across the platform stack and youll be the person nudging us to make AI a practical everyday part of how we build and operate. Youll spot where AI can make the platform and the teams on it faster from AI-assisted developer tooling to agents that take real work off peoples plates and youll help turn those ideas into things people use with the guardrails and cost controls that make them safe to run.

What youll get to do in the role:

• Build and maintain the cloud infrastructure and tooling our product teams rely on across compute networking CI/CD and deployment on Kubernetes or Cloud Run.
• Champion AI agent adoption across the platform: spot where agents can take on real work prototype it and turn the promising work into supported reusable capabilities teams can build on.
• Build the foundations for AI agents: integrations with Claude the tools and services agents call and the orchestration that ties them together.
• Wire observability guardrails and cost controls into the AI agents and tooling we run so they stay safe and predictable in production.
• Work hands-on with the AI tooling and agent frameworks we build on Claude in particular which we use extensively and bring what you learn back to the wider team.
• Manage infrastructure with Terraform keeping environments consistent auditable and reproducible.
• Contribute to our Azure-to-GCP migration thinking especially what it means for our AI tooling and agents.
• Partner with product engineers to lower the barrier to using AI writing the docs templates and examples that drive adoption.

What we think youll need:

• Genuine enthusiasm for AI agents and a desire to help us adopt them well. You dont need a machine-learning or model-training background this is about building with tools like Claude not training or serving models but you should be keen to learn and to lead by doing.
• Strong GCP experience (Azure experience is nice to have)
• Comfortable with containerisation (Docker) and deployment on Kubernetes or Cloud Run.
• Infrastructure-as-code with Terraform.
• Proficient in at least one (C#) or Python.
• Observability experience with Datadog or cloud-native logging and monitoring (GCP Cloud Operations or Azure Monitor).
• A track record of driving adoption building things engineers use writing docs that get read.
• Comfortable leading technical decisions collaborating across teams and mentoring other engineers.