Principal AI Engineer - Microsoft Azure AI Foundry

Hackajob Ltd

Holbeck

On-site

GBP 61,000 - 101,000

Full time

2 days ago

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Benefits offered by this job

25 days bookable holiday

6% employer pension contribution

Aviva healthcare cover

£1000 flexibenefit allowance

life assurance

income protection

eye test and glasses

enhanced maternity and partner leave

Job summary

hackajob Ltd. partners with AND Digital to hire for a 12-month contract Principal AI Engineer role focused on Microsoft Azure AI Foundry. You will architect scalable Azure AI Foundry environments and integrate with OpenAI services, ensuring governance and security across the platform.

You’ll collaborate with AI, Cloud, Security and DevOps teams to deliver production-grade AI workloads, multi-region readiness and cost-effective operations.

Qualifications

5 years experience in Azure cloud architecture, engineering or platform engineering.
12 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.
One or more preferred certifications: Microsoft Certified: Azure Solutions Architect Expert (AZ-305) and/or Microsoft Certified: Azure AI Engineer Associate (AI-102).

Responsibilities

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.

Skills

Azure

AI/ML Platform

Enterprise Architecture

Security & RBAC

DevOps collaboration

Education

Microsoft certs (AZ-305, AI-102)

Tools

Bicep

Terraform

ARM templates

Azure Policy

OpenAI Service

Job description

Salary: £61,000 - 101,000 per year

Requirements

5 years experience in Azure cloud architecture, engineering or platform engineering.
12 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.
One or more preferred certifications: Microsoft Certified: Azure Solutions Architect Expert (AZ-305) and/or Microsoft Certified: Azure AI Engineer Associate (AI-102).

Responsibilities

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.

Technologies

AI
AI Agents
API
ARM
Architect
Azure
CI/CD
Cloud
DevOps
Flow
GitHub
Support
RBAC
Security
Terraform
Embedded
MLOps

hackajob is partnering directly with AND Digital to hire for this 12-month contract Principal AI Engineer role focused on Microsoft Azure AI Foundry. We are on a mission to close the worlds tech skills gap by combining human expertise, emerging technology and AI to deliver better outcomes faster. Since 2014, we have helped organisations solve complex challenges, build high‑performing teams and create lasting capability. We organise our people into regional Clubs of no more than 80 people and Practice Areas that foster collaboration, learning and belonging. Our culture is rooted in Wonder, Share and Delight, with a strong emphasis on curiosity, ambition, growth, inclusion and client‑centric delivery.