AI Development Specialist - Azure

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