Senior AI Engineer (Amazon Bedrock)

CreateFuture; Manchester; Hybrid; GBP 110,000 - 140,000; Full time

Benefits offered by this job

35 days leave (including bank holidays

Private medical insurance

Enhanced parental and adoption leave

Financial coaching + 5% pension match

40 hours of paid learning and developm

Job summary

CreateFuture in the UK is hiring a hands-on senior AI engineer embedded in client delivery teams to design, build, and operate production-grade agentic AI systems on AWS. You’ll own end-to-end multi‑agent pipelines, knowledge bases, and orchestration frameworks.

The role sits at the intersection of AI engineering and cloud architecture, requiring real‑world production experience with Bedrock, MCP servers, RAG pipelines, and tool integration.

Qualifications

• Hands-on experience deploying production-grade AI agents.
• Experience with multi-agent orchestration and knowledge bases.
• Strong Python and AWS background.
• Familiarity with Bedrock and related AWS AI services.

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 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

Agentic AI

Multi-agent systems

Python

LLM integration

Prompt engineering

Tools

Amazon Bedrock

Strands Agents

MCP (Model Context Protocol)

AWS Lambda

DynamoDB

S3

CloudWatch

Job description

Working at CreateFuture

CreateFuture is an

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.

Our UK Benefits

• 35 days leave (including bank holidays).
• Private medical insurance.
• Enhanced parental and adoption leave.
• Financial coaching + 5% pension match.
• 40 hours of paid learning and development.

View our full list of UK benefits.

CreateFuture is a Great Place to Work-Certified™ company and has won Best Workplaces UK multiple years in a row.

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 & Experience

• Amazon Bedrock at scale — hands‑on implementation of agents, knowledge bases, and model inference in production environments, not limited to basic API calls
• Agentic AI and multi‑agent systems — direct experience designing and deploying agent pipelines with real orchestration complexity
• MCP (Model Context Protocol) — built or integrated MCP servers in a production or near‑production context
• Strands Agents — familiarity with the framework and its application to agentic workflows on AWS
• RAG implementation — knowledge base design, chunking strategy, vector store configuration, and retrieval evaluation
• Python — strong, applied proficiency in an AWS and AI context
• AWS infrastructure — working knowledge of Lambda, DynamoDB, and S3 as components of AI system backends
• Observability — experience instrumenting AI systems with tracing, logging, and monitoring tooling (CloudWatch preferred)
• LLM integration — prompt engineering, tool/function calling, context window management, and output parsing
• AWS certifications desirable, particularly the AI/ML Specialty

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.

Our hiring process

We try to keep our hiring process clear, fair and respectful of your time. We aim to get back to everyone who applies and we will be upfront about where you are in the process.

It Usually Looks Like This

• Call with our Talent Acquisition Team
• Role specific capability interview

Depending on the role, we might also ask you to do a short presentation, a practical or technical task or have a values focused conversation. We will explain what is involved before anything happens.

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.