Gen AI Engineer

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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