Skywaves Rise
Artificial Intelligence Engineer
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Role: AI Developer
Location: Leeds, UK
Mode: Hybrid (3 days)
Contract Duration: 6+ Months
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The Role
To design, build, and operationalize Copilot-based and LLM-powered solutions, focusing on secure engineering, enterprise integration, and reusable accelerators that reduce vendor dependency and increase internal delivery velocity.
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Mandatory Requirements
- Hands-on experience with Copilot.
- Strong background in Data Engineering or broader Engineering disciplines.
- Solid, hands-on experience within the Microsoft ecosystem, specifically:
- Building and integrating agents within Copilot Studio.
- Leveraging PowerApps, Dataverse, Fabric, and Azure.
- Utilizing Foundry environments.
- Practical experience integrating agents, APIs, and associated components within these specific platforms.
Your Profile
Essential Skills/Knowledge/Experience
- 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 optimization.
- Experience implementing secure-by-design practices including authentication, authorization, data protection, and auditability.
- Experience working within Microsoft Foundry-style model and agent engineering, including LLM orchestration, RAG component optimization, 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 behavior, 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.
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