ai engineer for agentic AI systems

CreateFuture is an AI-native consulting partner that works with organisations such as PayPal, adidas, NatWest, FanDuel and Money Saving Expert to build digital products and services. It develops AI solutions, shapes delivery, builds go-to-market strategies and creates practices that support its people.

• 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 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 protocol to enable interoperability between agents across systems and workflows;
• Instrument AI systems with observability and tracing tooling, including 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 AWS Lambda, DynamoDB and S3 to build scalable, cost-efficient backends for AI workloads;
• Collaborate with client engineering and product teams to translate requirements into agent architectures;
• Contribute to technical roadmaps and AI strategy.

• Hands-on experience implementing Amazon Bedrock agents, knowledge bases and model inference in production environments, beyond basic API calls;
• Direct experience designing and deploying agent pipelines with real orchestration complexity;
• Experience building or integrating MCP servers in a production or near-production context;
• Familiarity with Strands Agents and its application to agentic workflows on AWS;
• Knowledge of RAG implementation, including knowledge base design, chunking strategy, vector store configuration and retrieval evaluation;
• Strong, applied Python proficiency in an AWS and AI context;
• Working knowledge of AWS Lambda, DynamoDB and S3 as components of AI system backends;
• Experience instrumenting AI systems with tracing, logging and monitoring tooling;
• Experience with prompt engineering, tool/function calling, context window management and output parsing;
• Nice to have: AWS certifications, particularly the AI/ML Specialty.

• 35 Days of leave, including bank holidays;
• Private medical insurance;
• Enhanced parental and adoption leave;
• Financial coaching and 5% pension match;
• 40 Hours of paid learning and development;
• Flexible working arrangements;
• Occasional travel to client sites or CreateFuture offices when needed.