machine-learning-engineer

Access Azure AI Search, Speech, OpenAI, and Document Intelligence services through unified MCP tools. AI Search supports full-text, vector, hybrid, and semantic search with built-in AI enrichment for entity extraction and OCR Speech service handles real-time and batch speech-to-text transcription, text-to-speech synthesis with neural voices, and speaker diarization MCP tools provide direct access: azure__search for index queries and azure__speech for transcription and synthesis SDK references available for Python, TypeScript, .NET, and Java across all services; enable via /azure:setup or /mcp if MCP is not active

End-to-end deployment, evaluation, and management of AI agents on Microsoft Foundry. Covers the complete agent lifecycle: creation from starter samples, containerization and ACR push, hosted or prompt agent deployment, invocation, batch evaluation, and prompt optimization Includes specialized sub-skills for deploy, invoke, observe (evaluation and prompt optimization), trace analysis, troubleshooting, and dataset curation from production traces Supports project and resource provisioning, RBAC management, quota tracking, and model deployment with intelligent routing across regions and SKUs Requires .foundry/agent-metadata.yaml as the source of truth for environment-specific configuration, datasets, and evaluation test cases

Configure Azure API Management as an AI Gateway for models, MCP tools, and agents with built-in governance policies. Supports semantic caching (60-80% cost savings), token rate limiting, content safety filtering, and jailbreak detection across AI backends Add Azure OpenAI, AI Foundry models, or convert existing APIs to MCP tools as managed backends with load balancing Includes five core policy categories: authentication, semantic cache lookup, token limits, content safety, and token metrics for observability Requires Azure CLI for configuration and testing; integrates with managed identity for secure backend access

Build and deploy GitHub Copilot SDK applications to Azure with flexible model configuration. Three scaffolding paths: create new greenfield projects, add SDK services to existing repos, or deploy existing SDK apps with Azure infrastructure Supports three model configurations: GitHub's default models, specific GitHub models via discovery, or bring-your-own-model (BYOM) on Azure with DefaultAzureCredential authentication Includes complete templates with Express/TypeScript API, React/Vite frontend, Bicep infrastructure, Docker support, and token management scripts Deploy workflow uses azure-prepare, azure-validate, and azure-deploy steps; requires Docker and respects existing AGENTS.md configuration in user repos