Senior Platform Engineer - Azure Data & AI Platform

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.

Senior Platform Engineer - Azure Data & AI Platform

The Role

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 DevSecOps 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.

What You'll Actually Do

Azure Platform Engineering (40%)

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 ID, service principals, managed identities

Establish monitoring, logging, and alerting with Azure Monitor, Log Analytics, and Application Insights

Cost management and FinOps practices - keeping cloud spend under control without hamstringing teams

Databricks & Data Platform (30%)

Deploy and configure Azure Databricks workspaces 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

MLOps & AI Platform (20%)

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

DevSecOps & Platform Enablement (10%)

Creation of tools, features and dashboards for the developer platform

Build CI/CD pipelines in Azure DevOps or GitHub 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

What We Need From You

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

Databricks experience - you've deployed workspaces, configured Unity Catalog, optimized Spark jobs, managed costs

Infrastructure as Code - Terraform (preferred) or Bicep. You write modules, understand state management, know how to structure large IaC projects

CI/CD pipelines - Azure DevOps or GitHub Actions. You've built multi-stage pipelines with gates, approvals, and security scanning

Containerization experience – you know best practices when building and working with both application-based containers and containers holding ML/ AI models

Security-first mindset - you understand defense in depth, least privilege, network segmentation, and don't treat security as an afterthought

MLOps fundamentals - model training vs inference, experiment tracking, model versioning, deployment patterns

Python and/or PowerShell - for automation, tooling, and platform utilities

O bservability stack beyond basic metrics (distributed tracing, log aggregation patterns)

Strongly Preferred

Experience with Azure landing zones and CAF (Cloud Adoption Framework)

Microsoft Purview for data governance and cataloging

Experience with Delta Lake, Spark optimization, data quality frameworks

Azure networking certifications or equivalent deep knowledge

Container orchestration (AKS)

API design and management (API Management, App Gateway, Front Door)

What Actually Matters

Pragmatism over purity - you choose the right tool for the job, not the coolest one

Documentation discipline - you document as you build because you know future-you will thank today- you

Automation mindset - if you do it twice, you automate it

Everything-as-code – if it’s not in git, it doesn’t exist to you

Collaboration skills - you can translate between data scientists, engineers, and business stakeholders

Ownership mentality - you build it, you run it, you support it

Intellectual honesty - you say "I don't know" when you don't, and then you figure it out

What We Offer

Actual flexibility: Remote-first with occasional in-office travel for workshops/planning. We care about outcomes, not seat time.

Real learning budget: for conferences, training, certifications. We expect you to use it.

Tooling: You'll get the equipment and licenses you need to do the job properly.

Grown-up engineering culture:

PRs are required, branching is mandatory , tests matter

Blameless post-mortems when things break

Technical decisions driven by evidence and context, not politics or trends

We write RFCs for significant changes

Add the usual stuff here: How to apply, interview process.

What do we offer in return?

A career that you define. At Howden, we value diversity – there is no one Howden type. Instead, we’re looking for individuals who share the same values as us:

Our successes have all come from someone brave enough to try something new

We support each other in the small everyday moments and the bigger challenges

We are determined to make a positive difference at work and beyond

Reasonable adjustments

We're committed to providing reasonable accommodations at Howden to ensure that our positions align well with your needs. Besides the usual adjustments such as software, IT, and office setups, we can also accommodate other changes such as flexible hours* or hybrid working*.

If you're excited by this role but have some doubts about whether it’s the right fit for you, send us your application – if your profile fits the role’s criteria, we will be in touch to assist in helping to get you set up with any reasonable adjustments you may require.

*Not all positions can accommodate changes to working hours or locations. Reach out to your Recruitment Partner if you want to know more.

Permanent