Backend AI Engineer

About the role

The Physical Network Inventory team is responsible for maintaining the critical inventory systems used to plan, design, and build the Openreach network. These systems have played a key role in supporting Openreach's nationwide full fibre rollout across the UK. In this role, you will contribute to the design, development, and support of AI-powered solutions, including copilots, intelligent automation tools, and workflow systems that enhance engineering productivity, streamline operations, and deliver improved business outcomes.

What you’ll be doing

AI Development & Implementation
• Design, develop, and deploy AI-powered agents and automation solutions.
• Build and maintain integrations between AI solutions and enterprise applications using APIs and microservices.
• Implement AI capabilities within the Software Development Lifecycle (SDLC) for platform applications.
• Leverage AI-assisted development tools such as Kiro, GitHub Copilot, Cursor, and Amazon Q to improve productivity and code quality.
• Develop agent-based workflows that automate routine tasks and enhance decision-making processes.
Architecture & Design
• Contribute to the design and implementation of scalable AI solution architectures.
• Develop and maintain reusable prompt templates and prompt engineering patterns.
• Apply AI design patterns such as Retrieval Augmented Generation (RAG), ReAct, Prompt Chaining, and Planner-Executor workflows.
• Integrate AI solutions with existing applications, data sources, and business processes.
• Manage context and memory within AI applications to improve user experience and solution effectiveness.
Solution Development
• Develop AI-enabled services using Large Language Models (LLMs) and supporting technologies.
• Build and maintain APIs and services that enable AI agents to interact with internal and external systems.
• Create agentic workflows to automate operational tasks and business processes.
• Support testing, debugging, optimisation, and continuous enhancement of AI-powered applications.
• Ensure compliance with engineering standards, security requirements, and development best practices.
AI Evaluation & Continuous Improvement
• Monitor and evaluate AI solution performance using defined metrics and benchmarks.
• Measure and improve solution accuracy, task success rates, response latency, runtime efficiency, and operational costs.
• Analyse user feedback to identify opportunities for enhancement and optimisation.
• Conduct prompt tuning, testing, experimentation, and performance analysis to improve AI outcomes.
• Contribute to ongoing performance monitoring and reporting initiatives.
Collaboration
• Work closely with senior engineers, architects, and product teams to deliver AI initiatives.
• Participate in Agile ceremonies, technical reviews, and design discussions.
• Share knowledge and promote best practices in AI engineering and development.
• Support the adoption of AI-enabled ways of working across the programme and wider organisation.

Essential Skills / Experience

• Experience with AI-assisted software development tools such as GitHub Copilot, Amazon Q, Cursor, or Kiro.
• Strong understanding of prompt engineering and creating structured, reusable prompts.
• Knowledge of Retrieval Augmented Generation (RAG) concepts and applications.
• Experience developing APIs and microservices.
• Working knowledge of Large Language Models (GPT, Claude, Gemini, etc.).
• Experience implementing AI capabilities within the Software Development Lifecycle (SDLC).
• Understanding of Model Context Protocol (MCP) concepts.
• Ability to design cost-efficient and scalable AI solutions.
• Knowledge of AI design patterns including ReAct, Planner-Executor, and Chain-of-Thought prompting.
• Proficiency in Python and/or Node.js.
• Experience with vector databases such as Pinecone, FAISS, or Azure AI Search.
• Experience with Azure AI Services and/or AWS Bedrock.
• Understanding of software engineering best practices, including testing, security, and maintainability.
• Experience building APIs, integrating external services, and developing AI-powered applications.
• Experience working in Agile delivery environments.

Desirable Skills / Experience

• Experience with AI-native IDEs and modern AI development tools.
• Exposure to agent-based and workflow-driven AI solutions.
• Knowledge of practical AI design pattern implementation in enterprise environments.
• Experience using AI-assisted coding tools to improve development productivity and code quality.
• Experience supporting or developing LLM-based applications and prompt engineering solutions.
• 4-6 years of software engineering experience, including 2-3 years working with AI-enabled solutions.
• Willingness to learn, experiment with, and adopt emerging AI technologies and best practices.