Role Purpose Lead the end-to-end delivery of enterprise-grade AI solutions on Microsoft Azure. This role is responsible for designing, building, and deploying scalable, secure, and production-ready AI systems-from data ingestion and model development through to deployment, monitoring, and optimisation-aligned to enterprise architecture and regulatory standards. Key Responsibilities Design, develop, and deploy AI/ML solutions leveraging Microsoft Azure AI services (Azure OpenAI, AI Search, Azure Machine Learning). Build and expose secure, scalable APIs and integrate AI solutions into enterprise platforms using Azure API Management and event-driven architectures (Event Hub). Develop and maintain robust data pipelines and ensure seamless integration with enterprise data platforms. Containerise applications and deploy across Azure environments (AKS, App Services, Azure Functions) using modern CI/CD pipelines. Implement observability, monitoring, and performance tuning to ensure reliability, scalability, and cost efficiency of AI workloads. Apply best practices in security, governance, and Responsible AI, ensuring compliance with banking and regulatory standards. Collaborate with cross-functional teams (engineering, data, architecture, business) to deliver high-impact AI solutions. Produce and maintain architecture documentation, technical designs, and operational runbooks. Core Technology Stack AI & Machine Learning Azure OpenAI Service, Azure AI Studio, AI Search, Azure Machine Learning Data & Integration Azure Data Lake, Synapse Analytics / Microsoft Fabric, Data Factory Event Hub, Azure API Management Compute & Hosting Azure Kubernetes Service (AKS), Azure Functions, App Services Infrastructure as Code (Bicep, ARM, Terraform) DevOps & MLOps Azure DevOps / GitHub Actions MLflow, monitoring & telemetry dashboards Languages & Frameworks Python (essential) C#/.NET or Node.js/TypeScript LLM frameworks such as LangChain or Semantic Kernel Required Experience 7+ years' experience in software engineering and/or machine learning engineering Minimum 3 years delivering production AI solutions on Microsoft Azure Proven track record of deploying end-to-end AI systems in enterprise environments Strong experience in designing and implementing Retrieval-Augmented Generation (RAG) solutions Solid understanding of prompt engineering, LLM optimisation, and performance tuning Experience in data engineering and secure system integration patterns Demonstrated experience working with architecture artefacts (diagrams, documentation, runbooks) Knowledge of Responsible AI, governance, and regulatory compliance (advantageous in b