About the position
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 banking environments)
Core Competencies
- Strong problem-solving and analytical thinking
- Outcome-driven with a focus on delivering business value
- Excellent communication and stakeholder engagement skills
- Ability to operate effectively in complex, cross-functional environments
- Leadership capability with a mentoring mindset
- Comfortable working in ambiguity with an iterative, agile delivery approach
Nice to Have
- Experience with Microsoft Fabric and/or Databricks on Azure
- Financial services or banking domain experience
- Azure AI Engineer Associate (or similar) certification
Qualifications
- Degree in Computer Science, Engineering, Mathematics, or a related field
- Equivalent practical experience will be considered
Desired Skills:
- Azure AI
- Azure OpenAI
- Azure Machine Learning
