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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