Senior Platform Engineer - Azure Data & AI Platform

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.