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 Accountabilities
Role Specific Accountabilities
* Design, 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 priorities
* Build the capability & capacity of functional resources to drive sustained commercial success
* Interpret & communicate the priorities for the Function, motivating and developing a high performing team
* Own functional priorities, applying specialist expertise to put the customer at the heart of everything and drive a profitable business
* Initiate and develop critical external and internal relationships which create value, collaborating to deliver commercial and customer priorities
* Uphold corporate legal & regulatory responsibilities
* Implement and manage transformation activity & harness innovation to create a high performing & sustainable business
Qualifications
Functional/Technical (Role Specific)
Essential
* Higher education qualification (or equivalent experience) in Ethics, Law, Risk Management, Social Sciences, Data/Computer Science or relevant field
* Proven 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 principles
* Use 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 competitors
* Understanding of how financial services organisations operate and the associated regulatory environment, or other regulated industries
* Awareness of the Mutual Sector and the needs and interests of Members