Description
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.
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
- 5+ years platform/infrastructure engineering
- Deep Azure knowledge - networking, IAM, storage, compute, PaaS services
- Databricks experience - you've deployed workspaces, configured Unity Catalog, optimized Spark jobs, managed costs
- Infrastructure as Code - Terraform (preferred) or Bicep
- CI/CD pipelines - Azure DevOps or GitHub Actions
- 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
- 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)
What We Offer
- Actual flexibility: Remote-first with occasional in-office travel for workshops/planning
- Real learning budget: for conferences, training, certifications
- Tooling: You'll get the equipment and licenses you need to do the job properly
- Grown-up engineering culture