My client, a leader in Financial Services, are looking for an AI Engineer with hands‑on implementation of Gen AI solutions required on a contract basis for 6 months plus extensions, with 2 days per week in London. Our client is looking at a pay rate of £650 - £700 per day inside IR35. Role & Responsibilities Working with business and technology stakeholders and contributing hands‑on to the delivery of Gen AI‑enabled application development across the EMEA region. Key Responsibilities

Hands‑on implementation of Gen AI solutions based on approved designs and architectural standards Develop, fine‑tune and optimise LLMs and Gen AI models for production use Build, maintain and enhance prompt engineering strategies and retrieval‑augmented generation (RAG) pipelines Collaborate with architects, cloud teams and business stakeholders to ensure secure, scalable and robust deployments Conduct unit and integration testing, troubleshoot issues and support smooth releases into production Maintain accurate documentation for code, processes and configurations Monitor, analyse and improve model performance post‑deployment Partner with Cloud teams to enable secure and maintainable Gen AI solutions aligned with enterprise architecture standards Monitor the evolving AI landscape and identify relevant applications for the organisation Guide development teams through solution implementation, testing and deployment Provide guidance and mentorship to other team members

Required Skills

Experience delivering architectural or Gen AI solutions within a multi‑entity, multi‑regional financial services environment Proven experience working with senior stakeholders across business and technology functions Experience architecting and delivering Gen AI solutions across cloud platforms (Microsoft Azure, AWS, OCI, etc.) Strong expertise in developing Gen AI solutions in Python using frameworks such as LangChain, LangGraph, Microsoft Agents SDK or similar Experience with CI/CD pipelines and MLOps practices for AI solutions Hands‑on experience in model development and fine‑tuning Deep expertise in prompt engineering and RAG solution design Performance monitoring and optimisation of AI models Experience developing and deploying APIs using API Gateway Familiarity with Azure cloud services for LLM deployments including App Service, Azure Functions and Logic Apps Experience with Azure AI and data services including Azure OpenAI, Azure Cognitive Services, Azure AI Search, Azure Cosmos DB, Azure Data Lake, Microsoft Purview, Power Platform and Microsoft Graph Expertise in LLM integration patterns such as MCP and A2A Experience designing solutions integrating on‑premise and cloud systems within regulated environments Experience using GitHub Copilot or similar AI‑assisted development tools

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