Senior Software Engineer (AI Specialty)

The Senior AI Engineer is a senior technical role within the Technology function, sitting at the intersection of applied AI and enterprise engineering delivery. The individual will work at the heart of the engineering team responsible for building and maintaining enterprise technology platforms, bringing deep AI expertise to bear on how those platforms are

designed, built, and continuously improved. The role carries broad technical

accountability across both near-term delivery execution and the longer-term enterprise AI capability agenda.

This is a role for a technically credible individual who can operate across engineering delivery, enterprise architecture, and applied AI simultaneously - someone equally comfortable shaping AI strategy at the executive level and working alongside engineering teams to ensure solutions are built and shipped with rigour and consistency.

Responsibilities

1. Engineering Delivery & Technical Excellence

- Drive end-to-end delivery across the engineering portfolio, owning planning, prioritisation, and execution from inception to production.

- Set and uphold engineering standards across the team, ensuring consistency, quality, and technical rigour across all workstreams.

- Work closely with the Head of Technology to align engineering delivery with enterprise strategy and business priorities, translating strategic direction into executable technical roadmaps.

- Foster a culture of technical excellence, accountability, and continuous improvement within the engineering function.

- Support the growth and development of engineers across the team through hands-on technical guidance, code reviews, and knowledge sharing.

2. Enterprise AI Strategy & Execution - Define, own, and execute the organisation’s enterprise AI strategy across functions, platforms, and business units.

- Identify and prioritise AI opportunities that drive measurable improvement in operational efficiency, customer experience, and business performance.

- Lead the design and deployment of GenAI, LLM, and applied AI solutions at enterprise scale, ensuring they are production-grade, reliable, and governed.

- Establish AI engineering standards, integration patterns, and governance frameworks that enable consistent and responsible AI adoption across the organisation.

- Engage executive and business stakeholders to translate AI capabilities into practical, high

-impact solutions, communicating technical concepts clearly at every level.

- Maintain a forward-looking view of the AI landscape, evaluating emerging tools, models, and frameworks for enterprise applicability.

3. AI-Enabled Engineering Delivery

- Champion and embed AI-assisted engineering tools and practices across the team, including AI-powered code generation, testing automation, and developer productivity tooling.

- Design and implement AI features directly within enterprise platforms and customer-facing digital products, working across the full engineering stack.

- Implement and oversee MLOps pipelines, CI/CD automation, and AI deployment infrastructure to support reliable, scalable solution delivery.

- Ensure model monitoring, performance optimisation, and continuous improvement processes are embedded as standard engineering practice.

4. AI Platform & Infrastructure

- Oversee the design and evolution of scalable AI infrastructure, including cloud native platforms, vector databases, LLM orchestration frameworks, and retrieval augmented generation (RAG) pipelines.

- Drive standardisation of AI engineering frameworks, reusable service patterns, and deployment practices across the team.

- Contribute to the broader enterprise data and analytics ecosystem, ensuring AI use cases are well-supported by underlying data infrastructure.

5. Governance, Risk & Responsible AI

- Ensure all AI and technology solutions comply with enterprise cybersecurity, data privacy, regulatory, and governance standards.

- Implement responsible AI practices across the engineering function, including explainability, fairness, bias monitoring, and model accountability.

- Collaborate with Risk & Compliance to design and embed enterprise controls around AI adoption and deployment.x

- Maintain oversight of the AI governance framework, ensuring it evolves in line with regulatory developments and the organisation’s risk appetite.

Requirements

Qualifications and Experience

• Bachelor’s degree in Computer Science, Software Engineering, Artificial Intelligence, Data Science, or a related discipline.
• 8-12 years of progressive experience in software engineering, AI engineering, or a combination of both, with increasing technical scope and responsibility.
• Proven experience designing, building, and deploying production-grade AI solutions, with strong depth in Generative AI, Large Language Models (LLMs), and enterprise AI applications.
• Strong hands-on experience with C# and .NET, with the ability to develop, review, and establish engineering standards for enterprise backend systems.
• Strong proficiency in Python for AI/ML development, model integration, automation, and data pipeline development.
• Demonstrated experience with Generative AI and LLM applications, including prompt engineering, model integration, fine-tuning, and AI agent development.
• Practical experience designing and implementing Retrieval-Augmented Generation (RAG) solutions, vector search, embeddings, and knowledge retrieval systems.
• Experience with AI orchestration frameworks such as LangChain, LlamaIndex, Semantic Kernel, or equivalent technologies.
• Strong experience with RESTful APIs, microservices, enterprise integration patterns, and distributed systems.
• Proficiency in React and TypeScript, including the integration of AI capabilities into enterprise web applications.
• Hands-on experience with at least one major cloud platform, including Microsoft Azure, AWS, or Google Cloud Platform, preferably with experience deploying AI workloads.
• Experience with Docker, Kubernetes, CI/CD, DevOps practices, and infrastructure as-code.
• Experience working with SQL and NoSQL databases, including database design, optimisation, and integration with AI applications.
• Working knowledge of enterprise data platforms, data pipelines, event streaming technologies such as Kafka, and analytics ecosystems.
• Demonstrated experience working within a regulated, highly complex, or enterprise environment.
• Experience implementing AI governance, responsible AI principles, data privacy, cybersecurity, and risk controls in enterprise environments.
• Demonstrated ability to contribute to or define enterprise AI strategy and roadmaps, translating business objectives into practical AI solutions.