About this role

Own and evolve our Azure environment (Container Apps, Functions, storage, networking, data pipelines)
Scale our scraping and vision extraction systems as data volumes grow
Automate provisioning and operations (IaC where it fits, AI-assisted tooling where it’s faster)
Build observability that’s actionable: monitoring, alerting, and cost tracking that actually helps
Create CI/CD pipelines that let engineers ship agents fast and safely
Establish patterns and standards that make the platform easier to build on (templates, conventions, guardrails)
Architect for future needs: MLOps readiness, multi-vertical scaling, and agent orchestration at scale
Optimize Azure spend responsibly, including a $44 M commitment to use efficiently

5+ years building and running systems on Azure
experience scaling data acquisition and pipelines (ETL, scraping, feature engineering, reliability at volume)
MLOps/DataOps exposure (Azure ML, Databricks, experiment tracking, model lifecycle thinking)
ability to explain technical trade-offs to non-technical stakeholders in a crisp, pragmatic way
comfortable in Python for scripting, building internal tools, and creating small utilities

Senior Azure Platform Engineer – AI, Data

Key skills