Senior AI Engineer (Amazon Bedrock)

CreateFuture; City of Edinburgh; Hybrid; GBP 90,000 - 130,000; Full time

Benefits offered by this job

35 days leave

Private medical insurance

Enhanced parental and adoption leave

40 hours of paid learning and dev

Job summary

We are seeking a hands-on senior AI engineer to join CreateFuture, embedding in client delivery teams to design, build, and operate production-grade agentic AI systems on AWS. You will own end-to-end implementation of multi-agent pipelines, knowledge bases, and orchestration using Bedrock and related tools.

The role requires shipping real AI agents to production, with strong software engineering discipline, and collaboration with client teams to translate requirements into scalable architectures.

Qualifications

• Hands-on experience deploying production-grade agentic AI systems on AWS.
• Design and implement multi-agent pipelines and memory management.
• Experience shipping real AI agents to production, not prototypes.

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 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

Amazon Bedrock

Python

AWS

LLM integration

Observability

Knowledge bases

Vector stores

Context management

Tools

Strands Agents

Bedrock AgentCore

MCP (Model Context Protocol)

S3/DynamoDB/Lambda

Job description

Working at CreateFuture
CreateFuture is anAI-native consulting partnerwhere 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 & 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.

We look forward to your application!

Candidates for this role must be eligible to live and work in the UK.

We use Greenhouse’s AI-powered Talent Matching tool to compare your application against our job requirements.