Data Scientist
Posted Today by Randstad Digital
Negotiable
Outside
Hybrid
London Area, United Kingdom
Summary: The AI / LLM Engineer role focuses on designing and delivering enterprise-grade Generative AI and Copilot solutions within a cloud environment. The position requires hands-on experience in AI engineering, architecture, and platform delivery, emphasizing the creation of scalable and secure AI systems. The engineer will collaborate with cross-functional teams to implement and optimize AI solutions while adhering to secure development practices. This contract role is based in Leeds with a hybrid working arrangement.
Key Responsibilities:
- Design, build and deploy Copilot solutions, AI agents and LLM-powered applications
- Develop end-to-end LLM workflows (prompt orchestration, automation, integrations)
- Implement and optimise RAG pipelines, embeddings and vector search strategies
- Integrate AI solutions with enterprise systems, APIs and data platforms
- Build scalable and reusable components (prompt libraries, connectors, templates)
- Apply secure-by-design and responsible AI practices (guardrails, monitoring, auditability)
- Implement observability and monitoring (logging, telemetry, performance tracking)
- Troubleshoot and improve model performance, reliability and hallucination issues
- Collaborate with cross-functional teams across engineering, architecture and governance
Key Skills:
- Proven experience building LLM / Generative AI solutions in production
- Strong understanding of: RAG (Retrieval-Augmented Generation), Embeddings and vector databases, Prompt engineering and orchestration
- Hands-on experience with: Azure AI / Azure OpenAI, Copilot Studio or similar agent frameworks
- Experience integrating with APIs and enterprise systems
- Knowledge of secure development practices (authentication, data protection, compliance)
- Familiarity with DevOps, CI/CD, monitoring and cloud-native architectures
- Ability to debug AI behaviour (hallucinations, inconsistency, performance issues)
- Nice to Have: Experience in financial services or regulated environments, Exposure to AI governance, risk and compliance frameworks, Experience with agent frameworks (e.g. orchestration tools, Semantic Kernel, LangChain)
Salary (Rate): undetermined
City: Leeds
Country: United Kingdom
Working Arrangements: hybrid
IR35 Status: outside IR35
Seniority Level: undetermined
Industry: IT
AI / LLM Engineer (Copilot, Azure AI) Leeds (Hybrid – 3 days onsite) | Contract | Outside IR35 | Competitive Day Rate
We’re looking for a hands-on AI / LLM Engineer to design and deliver enterprise-grade Generative AI and Copilot solutions within a modern cloud environment. This role sits at the intersection of AI engineering, architecture, and platform delivery, with a strong focus on building scalable, secure, and production-ready AI systems.
What You’ll Be Doing
- Design, build and deploy Copilot solutions, AI agents and LLM-powered applications
- Develop end-to-end LLM workflows (prompt orchestration, automation, integrations)
- Implement and optimise RAG pipelines, embeddings and vector search strategies
- Integrate AI solutions with enterprise systems, APIs and data platforms
- Build scalable and reusable components (prompt libraries, connectors, templates)
- Apply secure-by-design and responsible AI practices (guardrails, monitoring, auditability)
- Implement observability and monitoring (logging, telemetry, performance tracking)
- Troubleshoot and improve model performance, reliability and hallucination issues
- Collaborate with cross-functional teams across engineering, architecture and governance
Key Skills & Experience
- Proven experience building LLM / Generative AI solutions in production
- Strong understanding of: RAG (Retrieval-Augmented Generation) Embeddings and vector databases Prompt engineering and orchestration
- Hands-on experience with: Azure AI / Azure OpenAI Copilot Studio or similar agent frameworks
- Experience integrating with APIs and enterprise systems
- Knowledge of secure development practices (authentication, data protection, compliance)
- Familiarity with DevOps, CI/CD, monitoring and cloud-native architectures
- Ability to debug AI behaviour (hallucinations, inconsistency, performance issues)
- Nice to Have Experience in financial services or regulated environments Exposure to AI governance, risk and compliance frameworks Experience with agent frameworks (e.g. orchestration tools, Semantic Kernel, LangChain)