AI Developer
Role details
Job location
Tech stack
Job description
- 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.
Requirements
Hands-on experience with Copilot. Ideally, candidates should have a strong background in Data Engineering or broader Engineering disciplines. It is essential that all candidates demonstrate solid, hands-on experience within the Microsoft ecosystem, specifically:
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Building and integrating agents within Copilot Studio.
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Leveraging PowerApps, Dataverse, Fabric, and Azure.
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Utilizing Foundry environments.
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Practical experience integrating agents, APIs, and associated components within these specific platforms.
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Higher education qualification (or equivalent experience) in Ethics, Law, Risk Management, Social Sciences, Data/Computer Science or relevant field
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Proven hands-on experience building solutions using LLMs, AI APIs, Copilot Studio or agent frameworks.
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Strong understanding of vector databases, embeddings, RAG architectures and retrieval optimisation.
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Experience implementing secure-by-design practices including authentication, authorisation, data protection and auditability.
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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.
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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.
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Familiarity with DevOps, CI/CD, IaC, observability, monitoring and modern engineering pipelines.
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Ability to translate complex requirements or user needs into scalable, maintainable technical solutions.
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Ability to debug unexpected AI or model behaviour, including hallucinations, variability and reliability issues.
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Strong documentation skills and ability to produce reusable code assets, templates and guidance.
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Collaborative working style with analysts, testers and architects throughout delivery.
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Comfortable learning and adapting to emerging AI technologies and engineering patterns.
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Excellent stakeholder management and communication skills, including senior-level engagement.
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Commercial awareness and a value-driven mindset.
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Familiarity with AI ethics, fairness, transparency and accountability principles
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Use of professional networks and external influencers with clear evidence of learning and development to build and maintain skills and expertise