Senior AI Engineer (Amazon Bedrock) in Edinburgh

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: A hands-on senior AI engineer embedded in client delivery teams, responsible for designing, building, and operating production-grade agentic AI systems on AWS. This role sits at the intersection of AI engineering and cloud architecture, owning the end-to-end implementation of multi-agent pipelines, knowledge bases, and orchestration frameworks using Amazon Bedrock and its surrounding ecosystem. The right candidate has shipped real AI agents to production — not just prototypes — and brings the rigour of a software engineer to a space that often lacks it. Key Responsibilities Design and implement production agentic AI systems on Amazon Bedrock, including multi-agent orchestration, memory management, and tool integration using Amazon Bedrock AgentCore and Strands Agents Build, integrate, and maintain MCP (Model Context Protocol) servers that expose capabilities to AI agents across client platforms Architect and implement RAG pipelines using Amazon Bedrock Knowledge Bases, managing vector stores, embeddings, and document ingestion from S3 and other sources Apply the A2A (Agent-to-Agent) protocol to enable interoperability between agents across systems and workflows Instrument AI systems with observability and tracing tooling — CloudWatch, spans, and traces — to support debugging, performance monitoring, and compliance requirements Integrate LLMs into client applications through prompt engineering, context management, and function/tool calling patterns Leverage serverless infrastructure — AWS Lambda, DynamoDB, S3 — to build scalable, cost-efficient backends for AI workloads Collaborate with client engineering and product teams to translate requirements into agent architectures, contributing to technical roadmaps and AI strategy Skills