Senior Platform Engineer - Azure Data & AI Platform

Howden is a global insurance group with employee ownership at its heart. Together, we have pushed the boundaries of insurance. We are united by a shared passion and no-limit mindset, and our strength lies in our ability to collaborate as a powerful international team comprised of 24,000 employees spanning over 56 countries. People join Howden for many different reasons, but they stay for the same one: our culture.

It’s what sets us apart, and the reason our employees have been turning down headhunters for years. Whatever your priorities – work / life balance, career progression, sustainability, volunteering – you’ll find like-minded people driving change at Howden.

We are looking for senior platform engineers to build and operate our Azure-based data and AI platform. This is hands‑on infrastructure and platform work – you'll be writing Terraform, designing network architecture, implementing MLOps (AI model) pipelines, and establishing Dev Sec Ops  patterns that engineering teams can use. This isn't a “thought leadership” or “strategy” role. You'll be in the code, in the CLI, and in the infrastructure daily.

• Design and implement Azure landing zones, management groups, and subscription architecture
• Build and maintain hub‑spoke network topologies with proper segmentation and security controls
• Implement Azure Policy, RBAC, and governance frameworks that balance security with developer productivity
• Manage identity and access using Entra , service principals, managed identities
• Establish monitoring, logging, and alerting with Azure Monitor, Log Analytics, and Application Insights
• Cost management and Fin Ops practices - keeping cloud spend under control without hamstringing teams

• Deploy and configure Azure Databricks work spaces with Unity Catalog for data governance
• Assisting both data and AI teams with pipeline development
• Establish Databricks best practices: cluster policies, job scheduling, notebook standards, workspace organization
• Integrate Databricks with ADLS Gen2, Azure SQL, Synapse, and other data services
• Set up and maintain CI/CD for Databricks notebooks, jobs, and infrastructure
• Work with data engineers on performance optimization, cost control, and platform capabilities

• Build ML model deployment pipelines using Azure ML, Databricks MLflow, or both
• Implement model versioning, experiment tracking, and model registry patterns
• Establish inference endpoints (batch and real‑time) with proper monitoring and governance
• Create reusable ML pipeline templates and infrastructure‑as‑code modules
• Integrate AI services (Azure OpenAI, Cognitive Services) into platform offerings
• Implement responsible AI guardrails: model monitoring, bias detection, explainability

• Creation of tools, features and dashboards for the developer platform
• Build CI/CD pipelines in Azure Dev Ops or Git Hub Actions with proper security scanning
• Implement shift‑left security: SAST/DAST, dependency scanning, infrastructure scanning, secrets management
• Establish infrastructure‑as‑code standards with Terraform (or Bicep), including modules and policy enforcement
• Create self‑service tooling and automation for common platform tasks
• Write documentation that engineers will read and use
• Participate in on‑call rotation for platform incidents

• Required 5+ years platform/infrastructure engineering - you've built production platforms, not just prototypes
• Deep Azure knowledge - networking, IAM, storage, compute, PaaS services. You know the difference between service endpoints and private endpoints and when to use each
• Data…