Senior Platform Engineer

Who are we? 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-limits 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. Role Senior Platform Engineer – Azure, Data & AI Location: United States (Remote) Reports to: AI Lead Employment Type: Full-time, Exempt Direct Reports: None Who are we? Howden is a collective, a group of talented and passionate people all around the world. Together, we have pushed the boundaries of insurance. We are united by a shared passion and no-limits mindset, and our strength lies in our ability to collaborate as a powerful international team comprised of 20,000 employees spanning over 100 countries. Our people are our biggest asset as well as our largest shareholder group and are everything that makes us unique; our inclusive culture, the quality service we offer our clients, and our continued growth, all come from our people’s first approach. There's no such thing as individual success. We all need to play our part, contributing our skills and experience to make a true difference. That's Howden. Why work at Howden? We have always been employee-owned and driven by entrepreneurial spirit. Right from the beginning, we've focused on employing talented individuals and empowering them to make a difference for clients and the company, whilst building successful and fulfilling careers at the same time. Simply put, we hire talented specialists and give them what they need to make a difference. 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 disappointed head-hunters for years. Whatever your priorities, work/life balance, career progression, sustainability, volunteering, you'll find like-minded people driving change at Howden. What is the role? The Senior Platform Engineer builds and runs the Azure platform that Howden's

US AI

and data workloads sit on. This is a hands-on engineering role: you will design, code, and operate the landing zones, the data platform, the AI runtime services, and the networking, identity, and security controls that let engineering teams ship AI capability safely and quickly. The scope spans three connected layers. The Azure foundation: subscriptions, landing zones, networking, private connectivity, identity, and policy, all expressed as Terraform. The data platform: Databricks, Lakehouse storage, Unity Catalog, ingestion and orchestration, and the governed data products that AI systems consume. The AI platform: Azure AI Foundry, the AI Gateway, agent runtime and hosting, model deployment and quota management, and the control surfaces such as Microsoft Agent 365 and the agent registry that make agent estates visible and governable. You will work from architecture direction set by the Leads, Group Architecture, and InfoSec, and you will own the implementation end to end. Expect to spend most of your time in Terraform, in pipelines, and in Azure, with a heavy emphasis on agent-assisted development: using coding agents and AI development tooling to move faster than a conventional infrastructure pace and knowing when to trust the output and when to rewrite it. This role is self-directed. You will be handed an outcome and a rough shape, and you are expected to break it down, sequence it, unblock yourself, and deliver production-quality work collaborating with other team members. What success looks like

• Azure environments for AI and data workloads provisioned entirely from code, repeatable across dev, non-production, and production, with no manual portal steps.
• A Databricks and Lakehouse platform that engineering and analytics teams use daily, with governed access, documented data products, and predictable cost.
• Azure AI Foundry, AI Gateway, and agent hosting running as a shared platform service with quota, routing, cost attribution, and usage telemetry that teams can self-serve against.
• Network, identity, and data boundaries implemented to Howden and Group InfoSec standards, evidenced rather than asserted, and accepted through architecture and security review without rework.
• Platform golden paths that let a product team stand up a compliant AI workload in days rather than weeks, using published modules, pipelines, and reference implementations.
• Agent-assisted development practices used daily and shared with the team as working examples: module scaffolding, policy generation, test harnesses, and repository conventions.
• Work delivered from a stated outcome with minimal direction, escalating early when something genuinely needs a decision above your level.
• Design input that changes outcomes: options, trade-offs, and working spikes brought to the Leads and architecture before decisions are locked. What will you be doing? Azure Platform Engineering
• Design, build, and operate Azure landing zones for AI and data workloads: subscription and management group structure, naming and tagging standards, Azure Policy, RBAC models, and cost boundaries.
• Provision and run the compute and hosting layer for AI services: Azure Container Apps, AKS, App Service, Azure Functions, and container registries, with sensible scaling, resilience, and resource configuration.
• Build shared platform services that product teams consume: API Management, Key Vault, Service Bus, Event Hubs, Storage, Cosmos DB, and managed identity patterns.
• Own capacity, quota, and region strategy for AI and data services, including data residency and data zone constraints.
• Keep environments reproducible. Anything created by hand in the portal gets replaced by code. AI Platform & Agent Runtime
• Build and operate Azure AI Foundry as a shared platform capability: resource and project topology per environment and data zone, model deployments, quota and throughput management, content safety configuration, and connection management.
• Implement and run the AI Gateway layer using Azure API Management AI Gateway and Foundry control plane capabilities: model routing, token-based rate limiting, semantic caching, cost attribution by team and workload, key and identity management, and usage telemetry.
• Stand up and operate the runtime that hosts AI agents: container and serverless hosting, orchestration runtimes, tool and MCP server connectivity, secrets and credential handling, and environment promotion.
• Implement agent identity and control surfaces including Microsoft Agent 365, Entra Agent ID, and the agent registry: registration, ownership, entitlement, lifecycle, and decommissioning of agents in the estate.
• Integrate the platform with governance and service management tooling so that agent inventory, approvals, and change records stay current rather than being maintained by hand.
• Build the guardrails that make self-service safe: landing-zone templates, workload onboarding automation, quotas, policy checks, and default observability wired in from day one. Data Platform Engineering
• Build and operate the Databricks platform: workspace topology, clusters and serverless compute, Unity Catalog, cluster policies, job orchestration, and cost controls.
• Implement Lakehouse storage and medallion layering on ADLS Gen2 with Delta, including partitioning, retention, and lifecycle management.
• Build ingestion and transformation pipelines from insurance source systems using Data Factory, Databricks workflows, Microsoft Fabric, or