Principal AI Engineer - Microsoft Azure AI Foundry (Contract)

About this role

Principal AI Engineer - Microsoft Azure AI Foundry
12 Month Contract

Who We Are
We’re on a mission to close the world’s tech skills gap.
We help organisations navigate the future of technology, combining human expertise, emerging tech and AI to deliver better outcomes, faster.
Since 2014, we’ve worked side-by-side with clients to solve complex challenges, build high-performing teams, and create lasting capability. As technology continues to evolve, we believe the most successful organisations will be those that combine the best of both: human ingenuity AND intelligent technology.
That belief is embedded in everything we do. We call it the genius of the AND: deep expertise AND practical delivery, innovation AND responsibility, ambitious work AND sustainable careers.
Through our Guide, Build and Equip approach, we help organisations embrace change, deliver meaningful impact, and develop the skills they need to thrive in an increasingly agentic world.

Role Overview
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.
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.
Key Responsibilities
Platform Architecture

• 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.
Governance, Risk & Compliance

• 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.
Security & Networking

• 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.
Scale, Resilience & Performance

• 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.
Cost Management & FinOps

• 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.
MLOps, Automation & Platform Engineering

• 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.

Experience

• 5+ years' experience in Azure cloud architecture, engineering or platform engineering.

• 1–2+ years' experience specifically focused on enterprise AI/ML platform engineering.

• Proven experience working on large-scale or enterprise Azure environments.

• Experience operating at Principal, Lead, Staff or equivalent senior technical level.

• Strong track record of translating business and technical requirements into enterprise architecture.

• Experience working collaboratively with AI Engineers, Cloud Engineers, Security, DevOps and Architecture teams.
Certifications – Preferred
One or more of the following certifications would be advantageous:

• Microsoft Certified: Azure Solutions Architect Expert (AZ-305)

• Microsoft Certified: Azure AI Engineer Associate (AI-102)

Key Competencies
The…