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Embark on a transformative journey as a GenAI Engineering Development Lead VP. At Barclays, our vision is clear—to redefine the future of banking and help craft innovative solutions. In this role, you will architect and ship agentic workflows, RAG, tool‑calling and orchestration, that shorten cycle times. You will raise quality and drive adoption with enterprise‑grade governance from day one, while designing and scaling agents across M365 Copilot/Copilot Studio, Azure and AWS, leveraging leading OpenAI and Anthropic LLMs with privacy and controls built in.
Job Responsibility:
Design, develop, implement, and support mathematical, statistical, and machine learning models and analytics used in business decision-making
Design analytics and modelling solutions to complex business problems using domain expertise
Collaboration with technology to specify any dependencies required for analytical solutions, such as data, development environments and tools
Development of high performing, comprehensively documented analytics and modelling solutions, demonstrating their efficacy to business users and independent validation teams
Implementation of analytics and models in accurate, stable, well-tested software and work with technology to operationalise them
Provision of ongoing support for the continued effectiveness of analytics and modelling solutions to users
Demonstrate conformance to all Barclays Enterprise Risk Management Policies, particularly Model Risk Policy
Ensure all development activities are undertaken within the defined control environment
Requirements:
Building architecture and practical knowledge of building solutions on Azure, AWS and M365 (Azure GenAI Services, SharePoint Online, MS Teams integrations, Power Automate, Power Apps
Design and own production solutions build using RAG, agents, tool-calling, prompt and workflow orchestration, MCP with explicit trade-offs for accuracy, latency, cost, and operability
Building on Azure, AWS and M365 (Azure GenAI Services, SharePoint Online, MS Teams, Power Automate/Power Apps), with practical knowledge execution across Azure OpenAI / Azure AI Foundry / AI Search / Azure ML and AWS Bedrock / SageMaker, plus core services (Entra/Managed Identity, Key Vault/Private Link, Functions/AKS, APIM
Building with enterprise guardrails—privacy, safety, regulatory compliance, secure‑by‑design patterns—and apply quality engineering, observability, evals and CI/CD throughout
Automation, integration & data: Proficient in process automation, workflow optimization, data catalogues and enterprise architecture design, with considerable API/integration skills across client–server, SOAP/REST and related protocols/security
Writing automated unit/integration tests, maintain reliability and performance, and work effectively with legacy code (refactoring and modernization)
Nice to have:
Willingness to learn – across business processes, current techniques, and new programming language paradigms
Product & stakeholder savvy – translate banker needs into clear problem statements, influence roadmaps, and communicate complex AI concepts simply
LLMOps & evaluation – practical knowledge with prompt design, automated evals (faithfulness/relevance), red‑teaming, and guardrails/content‑safety patterns
Cost/performance optimization – track token spend and latency, run A/B experiments, tune model routing, and meet metrics with telemetry‑driven decisions
Agentic & integration ecosystem – familiarity with orchestration frameworks (e.g., LangGraph/LangChain), MCP tool integration, vector stores (Azure AI Search/OpenSearch), and event‑driven APIs