JOB DESCRIPTION

The Azure Software Engineer (AI), working in a multi-disciplined team, requires a broad range of technical and soft skills to deliver intelligent cloud solutions effectively. These skills are categorised into the following domains:

Engineering & AI Development Skills

AI engineering is the core domain. Engineers are responsible for building, integrating, and operationalising intelligent solutions.

• AI-Driven Application Development - Design and build applications enhanced with AI capabilities using Azure OpenAI, Azure AI Services, and Azure Machine Learning
• Generative AI Implementation - Develop solutions leveraging large language models (LLMs), prompt engineering, embeddings, and retrieval-augmented generation (RAG).
• Machine Learning Integration - Integrate trained models into production systems using Azure ML endpoints and APIs.
• API Design & AI Integration - Build and expose APIs that integrate AI services into wider enterprise platforms.
• Data Pipeline Development - Design and implement pipelines for ingesting, processing, and transforming data for AI workloads.
• Model Operationalisation (MLOps) - Implement processes for versioning, deployment, monitoring, and life cycle management of ML models.
• Responsible AI - Ensure fairness, transparency, explainability, and governance in AI solutions.

Azure Platform & AI Services Skills

Strong knowledge of Azure's AI ecosystem and cloud platform is essential:

• Azure AI Services Expertise - Hands-on experience with Azure OpenAI, Cognitive Services, Azure Machine Learning, and AI Search.
• Cloud Architecture for AI - Design scalable AI architectures including data ingestion, model serving, and Real Time inference.
• Data Services - Work with Azure data platforms (Azure Data Lake, Synapse, Cosmos DB) to support AI workloads.
• Identity & Security - Secure AI systems using Azure AD, Managed Identities, and data protection best practices.
• Monitoring & Observability - Monitor models and applications using Application Insights and Azure Monitor, including model drift detection.
• Cost Optimisation - Manage and optimise AI workloads to balance performance with cost, especially for compute-intensive models.

Human Skills

Working in a multi-disciplinary AI team requires strong interpersonal capabilities:

• Problem Solving - Diagnose issues across AI models, data pipelines, and cloud infrastructure, identifying root causes effectively.
• Collaboration - Work closely with data scientists, data engineers, architects, and business stakeholders.
• Knowledge Sharing - Share AI and engineering knowledge across teams to build organisational capability.
• Adaptability - Keep up with rapidly evolving AI technologies, tools, and Azure capabilities.

Technical Skills

A strong technical foundation across software engineering, data, and AI is required:

• Programming Languages - Proficiency in languages commonly used in AI and cloud development (eg, Python, C#, JavaScript).
• AI/ML Frameworks - Familiarity with frameworks such as PyTorch, TensorFlow, or scikit-learn.
• Azure Cloud Platform - Deep expertise in Azure, particularly AI and data services.
• Containers & Kubernetes - Experience deploying AI workloads using Docker and Azure Kubernetes Service (AKS).
• Databases & Storage - Design and optimise both structured and unstructured data storage solutions.
• Version Control & CI/CD - Use Azure DevOps or GitHub for code, model versioning, and automated deployment pipelines.
• Data Engineering Foundations - Understanding of ETL/ELT processes and large-scale data processing.

Multi-discipline Enabling Skills

AI projects require cross-functional awareness:

• AI Operations (MLOps) - Manage AI solutions in production, including monitoring, retraining, and scaling.
• Security & Compliance - Ensure data privacy, regulatory compliance, and secure handling of sensitive AI data.
• Application Lifecycle Management - Contribute across the life cycle from experimentation to deployment and support.
• Architecture Collaboration - Work with architects to design scalable and responsible AI systems aligned to Azure best practices.

Process & Framework Knowledge

Modern AI engineering relies on structured processes and frameworks:

• Agile - Deliver AI features iteratively, incorporating feedback and experimentation.
• Scrum - Active participation in sprint delivery and planning cycles.
• DevOps & MLOps - Combine CI/CD with model life cycle management and data pipeline automation.
• Azure Well-Architected Framework - Apply principles across performance, reliability, security, and cost optimisation.
• Responsible AI Frameworks - Apply ethical AI principles and governance standards throughout development.
• SRE Principles - Ensure reliability and scalability of AI systems in production.

Remote working with occasional meetings in either Reading or Warton.

Inside IR35 £86-100/hr

10 months Contract

UK eyes only, so must be British National with Sole British passport

Must have active SC Security Clearance