A
AND Digital
Principal AI Engineer (Microsoft Azure AI Foundry, Contract)
Skills
Machine LearningAIGitHubTerraformAzure
2 months ago
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Important information
Contract type:
Permanent
Location:
Leeds, UK
Work mode:
Onsite
Published on:
5 September 2026
What they need
- We are seeking an experienced Principal AI Engineer to architect and deliver an enterprise-grade Microsoft Azure AI Foundry platform. The role will establish the foundational architecture, governance, security, scalability and cost-management capabilities required to support production AI and agentic workflows across the organisation
- You will operate at a senior technical level, working across Azure infrastructure, AI platform engineering, security, MLOps, FinOps and AI engineering teams to create a secure, scalable and reusable AI platform
- Architect and design scalable, resilient Azure AI Foundry environments aligned to enterprise cloud architecture and Azure Landing Zone principles
- Design the integration of Azure AI Foundry with Azure OpenAI Service, model catalogues, prompt flows, AI Search, vector databases and custom AI tooling
- Define reusable architecture patterns for AI workloads, including development, testing and production environments
- Establish platform standards covering resource structure, environments, deployment patterns, observability and operational management
- Work closely with AI Engineers to design and deliver AI Agents and agentic workflows that meet business and technical requirements
- Establish enterprise governance frameworks for AI workloads across Azure
- Define and implement Azure Policy, RBAC, resource controls and access-management standards
- Implement content safety and responsible-AI guardrails across AI workloads
- Establish model and prompt evaluation frameworks to support quality, safety and performance assessment
- Ensure comprehensive audit logging, monitoring and traceability across AI platform components
- Contribute to organisational standards for AI security, governance and responsible AI adoption
- Design secure Azure AI architectures covering both control-plane and data-plane security
- Implement Private Endpoints, VNets, Managed Identities and Microsoft Entra ID to secure AI services and associated data
- Apply zero-trust principles to AI workloads, APIs, data sources and platform services
- Define identity, authentication and authorisation patterns for AI applications, agents and platform users
- Ensure AI services are integrated into existing enterprise security and networking architectures
- Design resilient, highly available and, where required, multi-region AI deployment architectures
- Manage Azure OpenAI and AI platform capacity, including API rate limits, quotas and Provisioned Throughput Units (PTUs)
- Design architectures optimised for low-latency inference and reliable production workloads
- Establish performance monitoring, capacity planning and scaling strategies
- Define disaster recovery and business continuity patterns for critical AI services
- Establish cost-management frameworks for enterprise AI workloads
- Implement chargeback/showback models, resource tagging and cost allocation strategies
- Monitor and optimise AI consumption, including token usage and model utilisation
- Establish budgets, alerts, quotas and resource controls to manage consumption-based AI costs
- Work with engineering and finance stakeholders to identify opportunities to optimise AI platform expenditure without compromising performance or service quality
- Design and implement automated deployment pipelines for AI platform components and workloads
- Establish CI/CD processes using Azure DevOps and/or GitHub Actions
- Automate infrastructure provisioning and configuration using Bicep, Terraform or ARM templates
- Build automated processes for prompt-flow evaluation, model deployment, testing and release management
- Establish platform observability covering availability, performance, usage, cost and AI workload health- The successful candidate will combine deep Azure architecture expertise with hands-on experience of enterprise AI platforms and will play a key role in defining the technical standards and patterns for AI adoption
- Proven experience working on large-scale or enterprise Azure environments
- 1–2+ years’ experience specifically focused on enterprise AI/ML platform engineering
- Experience operating at Principal, Lead, Staff or equivalent senior technical level
- Experience working collaboratively with AI Engineers, Cloud Engineers, Security, DevOps and Architecture teams
- 5+ years’ experience in Azure cloud architecture, engineering or platform engineering
- Strong track record of translating business and technical requirements into enterprise architecture
- Ability to establish technical standards, patterns and governance frameworks
- Ability to translate emerging AI capabilities into production-ready enterprise architectures
- Exceptional Azure architecture and technical leadership skills
- A pragmatic, delivery-focused approach to enterprise AI adoption
- Strong understanding of enterprise security and cloud governance
- Hands-on engineering capability alongside strategic architectural thinking
- Strong understanding of AI platform scalability, reliability and cost optimisation
- Excellent communication skills, with the ability to engage technical and non-technical stakeholders
- Microsoft Certified: Azure AI Engineer Associate (AI-102)
- Microsoft Certified: Azure Solutions Architect Expert (AZ-305)
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