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About Us
CreateFuture is an AI-native digital consultancy that partners with global enterprise brands—including PayPal, adidas, NatWest, FanDuel, and Money Saving Expert—to build high-impact digital products and modern cloud platforms. Recognized as a multi-year Great Place to Work-Certified™ company, CreateFuture has grown to a 600+ person team by combining exceptional craftsmanship, human-centric culture, and cutting-edge technology.
Job Overview & Key Responsibilities
As a Senior Cloud Engineer at CreateFuture in Leeds, you will serve as a vital individual contributor and technical leader within client delivery squads. This role sits at the intersection of Infrastructure (40%), Software & Agentic Engineering (30%), and Data Engineering (30%). You will design, build, and deploy self-service developer platforms, AI/agentic infrastructure, and real-time data pipelines for enterprise clients operating in complex or highly regulated environments.
Key Responsibilities
- Platform Engineering & GitOps: Build independent, developer-centric platforms using Terraform, Kubernetes, and GitHub Actions to automate the provisioning of ephemeral, spec-driven deployment environments.
- AI & Agentic Infrastructure: Implement custom compute footprints, data pipelines, and workflow orchestrations using modern AI platforms like AWS Bedrock and AWS AgentCore.
- Data Engineering & RAG Architectures: Construct streaming data pipelines (AWS Kinesis, Kafka), data orchestration flows (dbt, Airflow), and vector database integrations for Retrieval-Augmented Generation (RAG) models.
- SRE, Observability & FinOps: Embed automated observability tools and cost-optimization utilities (e.g., Infracost, Karpenter) directly into CI/CD pipelines to ensure cost-efficient, resilient cloud operations.
- Consulting & Mentorship: Advise enterprise stakeholders, challenge legacy practices, establish engineering standards across CreateFuture, and mentor junior engineers within the Cloud capability.
Requirements & Desired Qualifications
- Core Skill Mix (Hybrid Pillar Balance):
- 40% Infrastructure & Automation: Extensive declarative IaC experience with Terraform, container orchestration with Kubernetes (nodes, clusters, pods), and automated CI/CD via GitHub Actions.
- 30% Software & Agentic Systems: Proficiency in modern scripting languages to build custom AI agents, API integrations, workflow orchestrators, and safety guardrails.
- 30% Data Engineering: Practical experience with real-time streaming (Kafka, Kinesis), orchestration (Airflow, dbt), and Vector DBs for RAG systems.
- Domain & Sector Expertise: Proven track record in client-facing consultancy roles and experience operating within strictly regulated sectors (Financial Services, iGaming, Banking) enforcing Zero Trust security frameworks.
- Certifications (Preferred): Associate-level cloud credentials (AWS Solutions Architect, Azure AZ-104), at least 1 Professional-level certification, or active progress toward specialized credentials (CKA, AWS ML Engineer Associate, or FinOps Certified Practitioner).
Job Role Comparison Table
| Feature / Aspect | Traditional DevOps Engineer | Senior Cloud Engineer (CreateFuture) | MLOps / AI Data Engineer |
|---|---|---|---|
| Primary Core Focus | Cloud infrastructure provisioning & CI/CD pipelines | Hybrid triad: Infrastructure (40%) + Software/Agents (30%) + Data (30%) | Model training pipelines, feature stores & LLM evaluations |
| Tech Stack Focus | Terraform, Docker, AWS/Azure, Jenkins | Terraform, Kubernetes, AWS Bedrock, Kafka, Vector DBs, Infracost | Python, PyTorch, Airflow, MLflow, Vector DBs |
| Delivery Model | Internal IT maintenance | AI-Native Consultancy & Client Enablement | Internal Data Science / AI Product Squad |
| Domain Scope | Server hosting & deployments | Autonomous platforms, RAG workflows & FinOps automation | Model accuracy & data pipeline throughput |
Key Benefits of This Role
- Generous Time-Off Allowance: 35 days paid annual leave (inclusive of bank holidays).
- Comprehensive Health & Wellness: Private medical insurance plus access to dedicated financial coaching.
- Family & Pension Support: Enhanced parental and adoption leave policies along with a 5% matching pension contribution.
- Dedicated Learning Investment: 40 hours of paid time allocated annually for personal learning, certifications, and development.
- Award-Winning Culture: Multi-year UK Best Workplace recipient with flexible hybrid and remote working options across hub offices (Leeds, London, Manchester, Edinburgh).
How to Prepare a CV for This Position
- Explicitly Mirror the 40/30/30 Hybrid Split: Structure your CV bullet points to demonstrate your balanced expertise across Infrastructure (Terraform, Kubernetes), Software/AI (Python, AI Agents, APIs), and Data Engineering (Kafka, Airflow, Vector DBs).
- Highlight AI & Platform Engineering Achievements: Clearly detail any projects involving self-service developer platforms, GitOps workflows, or LLM/RAG deployments (e.g., using AWS Bedrock or Vector databases).
- Quantify SRE & FinOps Results: Include concrete metrics showing how you optimized cloud spending or improved system reliability (e.g., “Embedded Infracost into GitHub Actions pipelines, reducing monthly AWS compute spend by 25%”).
- List Relevant Certifications & Regulated Experience: Prominently feature CKA, AWS Professional, or FinOps credentials along with any experience in banking, finance, or iGaming compliance.
Career Guide Tips for Growth
- Deepen Agentic & LLM System Design: Master frameworks for agentic guardrails, tool-calling APIs, and production RAG evaluation to stand out in the growing AI-native consultancy market.
- Embrace FinOps as a Native Engineering Discipline: Learn automated cloud cost tools (Karpenter, Infracost, Kubecost) to natively incorporate cost estimation into everyday IaC development.
- Develop Consulting & Advisory Presence: Refine your ability to communicate complex distributed system architectures and AI trade-offs to non-technical client stakeholders with confidence.
How to Prepare for the Interview
- Master Architecture Whiteboarding: Practice designing resilient, multi-region cloud platforms that incorporate both containerized workloads (Kubernetes) and AI streaming data pipelines.
- Prepare to Discuss the 2-Stage Hiring Process: Be ready for an initial Talent Acquisition alignment call followed by an in-depth capability interview focusing on code quality, system design, and consulting scenarios.
- Review AI/Agentic Infrastructure Concepts: Refresh your understanding of Vector DB retrieval mechanisms, AWS Bedrock agent workflows, and GitOps delivery pipelines.
Interview Questions & Guidance
1. “How do you design an ephemeral, self-service developer platform using Kubernetes and GitOps while maintaining strict security in a regulated bank or iGaming environment?”
How to answer: Explain combining Terraform for core cluster provisioning, ArgoCD or Flux for GitOps reconciliation, automated OPA/Gatekeeper policy enforcement for Zero Trust compliance, and ephemeral namespace management to ensure total isolation between environments.
2. “How do you integrate real-time streaming (e.g., Kafka or Kinesis) with Vector Databases to support low-latency Retrieval-Augmented Generation (RAG) applications?”
answer: Walk through ingestion pipeline design: chunking and embedding real-time events via orchestration tools (Airflow/dbt), storing vector representations in specialized stores (e.g., Pinecone orpgvector), and handling schema evolution and query latency under load.
3. “Can you give an example of how you embedded FinOps practices directly into a client’s deployment pipeline to prevent cloud cost drift?”
Use the STAR method: describe implementing Infracost or Karpenter within GitHub Actions PR workflows to provide pull-request level cost visibility, configuring automated auto-shutdown policies for non-production environments, and measuring total client cost savings.
Salary Insight
- Location: Leeds, West Yorkshire, United Kingdom (Hybrid / Remote options).
- Estimated UK Market Range: Senior Cloud Engineers with hybrid software, data, and AI-native platform engineering expertise in consultancy environments typically command base salaries ranging between £70,000 – £90,000 per annum (plus bonus and standard 35-day holiday/5% pension benefits).
How to Apply
To apply for the Senior Cloud Engineer role at CreateFuture:
- Online Application: Submit your CV and portfolio details directly through the official CreateFuture careers portal or partner recruitment channels.
- UK Office Hubs: Leeds, London, Manchester, Edinburgh (or remote option with occasional hub travel).
- Hiring Steps:
- Talent Acquisition Screening Call
- Role-Specific Capability Interview (may include a practical technical task, presentation, or values discussion)
- Career Page Link
- More related Vacancies.
Disclaimer
This job summary and career preparation guide has been independently crafted for informational and candidate interview preparation purposes. CreateFuture retains all primary rights to its official job postings, brand assets, benefits policies, and hiring decisions. Applicants should confirm specific contract terms, compensation packages, and remote policies directly with CreateFuture recruitment representatives.