Job Description
Purpose of the role: To design, build and operationalise Copilot‑based and LLM‑powered solutions, focusing on secure engineering, enterprise integration and reusable accelerators that reduce vendor dependency and increase internal delivery velocity.Key AccountabilitiesRole Specific AccountabilitiesDesign, develop and maintain Copilot solutions, intelligent agents, plugins, connectors and LLM workflows (e.g., Copilot Studio).Build scalable components (prompt orchestration, retrieval layers, automation flows, model interfaces, validation pipelines).Integrate with enterprise systems/APIs/data platforms, ensuring security, resilience and architecture alignment.Conduct rapid prototyping to validate feasibility, model behaviour, UX and performance.Implement secure‑by‑design and responsible AI practices (guardrails, controls, monitoring, auditability).Develop/optimise RAG components, embeddings, vector queries and metadata strategies for accuracy/reliability.Implement observability: logging, telemetry and LLM monitoring for quality and incident triage.Create reusable assets (prompt libraries, agent templates, connectors, test harnesses) and documentation.Translate design artefacts into build‑ready specifications and aligned solution designs.Co‑define test strategies and model performance thresholds with the AI Test Lead.Contribute to cross‑functional design/architecture reviews and standards evolution.Mentor colleagues and enable pro‑/low‑/no‑code teams to adopt AI safely.Ensure responsible AI principles (e.g., transparency, explainability, ISO42001) are incorporated into all development.Provide insight to support business cases, investment decisions, risk assessments, and prioritisation discussions at AI governance forums.Collaborate with teams to ensure all AI development work is implementable, sustainable and aligned to enterprise architecture.Maintain a library of development artefacts, patterns and re‑usable assets to support repeatability and uplift maturity across the AI Foundry.Managing escalations supporting the wider Data & AI Leadership team. Shared Accountabilities Translate Divisional priorities into plans and deliverables to deliver overall Group strategic prioritiesBuild the capability & capacity of functional resources to drive sustained commercial successInterpret & communicate the priorities for the Function, motivating and developing a high performing teamOwn functional priorities, applying specialist expertise to put the customer at the heart of everything and drive a profitable businessInitiate and develop critical external and internal relationships which create value, collaborating to deliver commercial and customer prioritiesUphold corporate legal & regulatory responsibilitiesImplement and manage transformation activity & harness innovation to create a high performing & sustainable business
Qualifications
Functional/Technical (Role Specific)EssentialHigher education qualification (or equivalent experience) in Ethics, Law, Risk Management, Social Sciences, Data/Computer Science or relevant fieldProven hands‑on experience building solutions using LLMs, AI APIs, Copilot Studio or agent frameworks.Strong understanding of vector databases, embeddings, RAG architectures and retrieval optimisation.Experience implementing secure‑by‑design practices including authentication, authorisation, data protection and auditability.Experience working within Microsoft Foundry‑style model and agent engineering, including LLM orchestration, RAG component optimisation, agent lifecycle management, versioning, monitoring, drift detection, and building reusable model/agent components governed under enterprise controls.Experience working with Microsoft Azure AI and cloud-native engineering, including integration with Azure AI services, secure deployment patterns, observability, telemetry, vector search and embeddings, and alignment with enterprise-grade cloud architectures used across the AI Foundry.Familiarity with DevOps, CI/CD, IaC, observability, monitoring and modern engineering pipelines.Ability to translate complex requirements or user needs into scalable, maintainable technical solutions.Ability to debug unexpected AI or model behaviour, including hallucinations, variability and reliability issues.Strong documentation skills and ability to produce reusable code assets, templates and guidance.Collaborative working style with analysts, testers and architects throughout delivery.Comfortable learning and adapting to emerging AI technologies and engineering patterns.Excellent stakeholder management and communication skills, including senior‑level engagement.Commercial awareness and a value‑driven mindset.Familiarity with AI ethics, fairness, transparency and accountability principlesUse of professional networks and external influencers with clear evidence of learning and development to build and maintain skills and expertise
Additional Information
Sector (desirable)Understanding of financial services industry, markets and competitorsUnderstanding of how financial services organisations operate and the associated regulatory environment, or other regulated industriesAwareness of the Mutual Sector and the needs and interests of Members
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