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 

• Observability 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.