Azure AI Decision Analysis Build - 06/02/2026 13:25 EST

I have an enterprise decision-analysis product on the roadmap and I need a senior-level Microsoft Azure engineer who is comfortable taking full ownership of the Azure development. The core requirement is an unsupervised machine-learning pipeline—deployed and managed in Azure—that can ingest raw data, uncover patterns autonomously, and feed those insights into the application’s decision engine.

Here is what I have in mind: data lands in an Azure Data Lake, is processed through Azure Databricks or Synapse, and the unsupervised models (clustering and anomaly-surfacing techniques are likely candidates) are trained and versioned in Azure Machine Learning. Model output then needs to be exposed via a RESTful endpoint, secured with Azure AD, and consumed by the front-end service. Everything must be CI/CD-enabled—GitHub Actions or Azure DevOps—so new experiments can move smoothly to production.

Deliverables
• Design document outlining the end-to-end Azure architecture
• Reproducible training notebooks / scripts for the unsupervised models
• Automated ML pipeline (data prep → training → registration → endpoint deployment)
• ARM/Bicep or Terraform templates plus YAML pipelines for one-click environment spin-up
• A brief hand-off session with usage instructions and next-step recommendations

Acceptance criteria
The solution must train, deploy, and return cluster assignments and anomaly scores on a supplied sample dataset within Azure; all resources build cleanly from the IaC templates; and the REST endpoint responds in <300 ms on the provided test workload.

If this end-to-end Azure AI build is squarely in your wheelhouse, let’s discuss timelines and milestone planning so we can get the decision engine into users’ hands quickly.