Senior Cloud Engineer, AI Platform SRE

Working at CreateFuture CreateFuture is an AI-native consulting partner where people do work that matters and are supported to do it well. We work alongside organisations such as PayPal, adidas, NatWest, FanDuel and Money Saving Expert, building digital products and services that make a difference while always putting people first.

We’re a team of creators. We write code, shape delivery, build go-to-market strategies, develop AI solutions and create the practices that support our people. We work side by side with our clients, challenging what’s not working and helping them to build the future. Our commitment to craft, quality, and culture has helped us scale to over 600 people in just a few years.

• 35 days leave (including bank holidays).
• Private medical insurance.
• Enhanced parental and adoption leave.
• 40 hours of paid learning and development.

Join us on our journey. Let’s create tomorrow, together, today.

About the role and team

AI platforms are still mostly run like prototypes. There's usually a Kubernetes cluster somebody set up in a hurry, no SLOs, no runbooks, and a cost line nobody can explain. We're engaging a Senior Cloud Engineer to bring proper SRE discipline to a major AI platform programme with a client in a high-traffic, heavily regulated consumer sector. You'll own how the platform runs. That means the Kubernetes infrastructure behind model serving, agent orchestration and batch inference. It means the CI/CD pipelines that ship models, agents, tools and prompt changes. It means the SLOs and on-call practice that make reliability an asset commitment rather than a hope, and the observability that makes AI-specific failure modes visible, including drift, silent quality regression, cost blowouts and agent loops.

You'll be the operational conscience of the programme. When a new capability is about to ship without limits, monitoring or a runbook, you're the person who says so. This is a role for someone who finds that work satisfying rather than thankless, and who wants to do it on a platform where the SRE patterns are still being written.

What you'll be doing

Technical Delivery & Implementation

• Kubernetes for AI workloads: Design, build and operate the Kubernetes infrastructure behind model serving, agent orchestration and batch inference, defined in Terraform and deployed through GitOps.
• CI/CD for non-deterministic systems: Build deployment pipelines suited to AI workloads, covering model rollouts, agent and tool updates, and prompt and configuration changes, with automated eval and regression checks before promotion.
• Reliability engineering: Define and track SLOs and SLAs for platform services, run incident response and root cause analysis, and write post-mortems people will read.
• On-call and runbooks: Take part in the programme's on-call rotation, and build runbooks clear enough that someone else can use them at 3am.
• AI-specific observability: Instrument latency, token usage, cost per request, model and agent error rates, retrieval quality and drift, with dashboards and alerting that surface problems before users do.
• FinOps: Embed cost estimation, anomaly detection and resource optimisation into the pipeline as a default rather than a monthly review.
• Security posture: Apply zero-trust networking, least-privilege access and secrets discipline to a platform handling sensitive data across multiple third-party model providers.

Client Delivery & Stakeholder Management

• Operability from day one: Work with the inference control plane, evaluation and knowledge platform workstreams so new capabilities arrive with monitoring, limits and a runbook, instead of acquiring them after the first incident.
• Technical advisory: Guide the client's architecture and AWS service choices on this platform, and build the working relationships that let that guidance land.
• Cost accountability: Understand the programme's budget context, build the cost‑benefit case for infrastructure decisions, and be able to justify spend to a finance audience.
• Risk and delivery: Spot emerging operational bottlenecks in the client's estate, raise them early, and plan and estimate your own stream accurately.
• Stakeholder communication: Explain reliability and cost trade-offs to technical and non-technical audiences, and pivot your framing to whoever is in the room.
• Fitting in fast: Work within the client's existing platform standards and tooling where they're sound, and make the case for change where they aren't.

Documentation & Handover

• Runbooks and decision records: Leave operational documentation, architecture decision records and Terraform the client's own engineers can maintain confidently.
• Knowledge transfer: Bring the client's engineers along in SRE practice as you go, so the reliability discipline holds after the engagement closes.
• Exit readiness: Treat a clean handover as part of the definition of done from the first sprint, not something arranged in the final fortnight.

Skills & Experience

Core Technical Capabilities

We're looking for a mix of platform and SRE (50%), software engineering (30%), AI systems operations (20%).

• Experience: 5+ years in DevOps, SRE or platform engineering, including real production ownership of Kubernetes rather than consuming a cluster someone else runs.
• Infrastructure as code: Strong Terraform, and production experience on AWS. GCP or Azure alongside it is welcome.
• Python: Solid enough for real automation and operational tooling, with sound engineering practice around it.
• Pipelines: Built and maintained CI/CD with GitHub Actions, GitLab CI, Argo CD, Jenkins or similar.
• Observability: Hands‑on with Datadog, Prometheus, Grafana or OpenTelemetry, and opinionated about what's worth alerting on.
• Incident management: Calm, methodical instincts under pressure, and a habit of fixing the class of problem rather than the instance.
• AI and ML operations: Experience operating model serving, LLM inference or MLOps pipelines in production is a strong plus. Familiarity with model APIs, vector databases, MCP or orchestration frameworks helps.
• Networking and security: VPC design, service mesh and zero‑trust patterns.

Domain & Sector Experience

• Regulated industries: Experience running platforms under strict compliance and data privacy controls, in iGaming, financial services or banking, is highly advantageous.
• Scale: Comfort with high-traffic, latency-sensitive systems where peak load is a sporting fixture rather than a gradual curve.
• Contract and consulting delivery: A track record of taking on operational ownership of an unfamiliar estate quickly, and being trusted with it.

Useful credentials

• Certified Kubernetes Administrator (CKA).
• AWS Solutions Architect Professional or DevOps Engineer Professional.
• FinOps Certified Practitioner (FOCP), which is a strong differentiator for this role.
• Terraform Associate.

What we’ll offer you

We trust people to do their best work. That means flexibility over rigid rules, impact over activity, and real investment in your growth both professionally and personally. You’ll be part of a supportive, and friendly culture, surrounded by smart, curious people who care deeply about what they do.

We offer flexible working, including hybrid and remote options. Our office hubs are located in Edinburgh, Leeds, Manchester, London and Bulgaria, with occasional travel to client sites or CreateFuture offices when needed.

We trust you to manage your time balancing collaboration with client time and focused work. What matters is the impact you have, not how busy you look.

Inclusion at CreateFuture

We believe diverse teams build better workplaces and better products. We want CreateFuture to be a place where people feel able to be themselves and do their best work.

If you need any adjustments or support during the application process, just. We will do what we can to help.

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