AI Engineer - Azure, Copilot Studio, GenAI

Azure AI Engineer Copilot

  • Design, build, and implement enterprise-grade GenAI and agentic AI solutions using Microsoft Azure services, including Azure OpenAI Service, Azure AI Foundry, Azure AI Search, Azure Machine Learning, Azure Functions, Azure Logic Apps, API Management, and Azure Kubernetes Service where applicable.

  • Develop and orchestrate intelligent agents for business workflows using Azure AI Foundry Agent Service, Microsoft Agent Framework, Semantic Kernel, LangChain/LangGraph, or other approved orchestration frameworks.

  • Implement Retrieval-Augmented Generation (RAG) patterns using Azure AI Search, vector indexing, metadata filtering, hybrid search, chunking strategies, embedding generation, and secure grounding with enterprise knowledge sources.

  • Build and extend Microsoft Copilot Studio agents, including topics, generative answers, agent flows, tools, connectors, prompts, knowledge sources, and integrations with Microsoft Teams, Microsoft 365 Copilot, Power Platform, and enterprise systems.

  • Integrate agents with enterprise applications and data sources using REST APIs, custom connectors, Azure Logic Apps, Power Automate, Microsoft Graph, Model Context Protocol (MCP), and secure tool/function calling patterns.

  • Implement multi-agent workflows, human-in-the-loop review patterns, task automation, and action-oriented GenAI use cases while ensuring traceability, approval controls, and business process alignment.

  • Develop secure APIs and backend services for LLM endpoints, agent tools, vector search, prompt execution, document processing, and conversational experiences using Azure Functions, App Service, AKS, or containerized services.

  • Develop and integrate FastAPI-based microservices to expose LLM capabilities, agent tools, RAG endpoints, prompt execution workflows, document processing services, and enterprise application integrations through approved Azure deployment patterns.

  • Implement document ingestion and content processing pipelines using Azure AI Document Intelligence, Azure Storage, Azure Data Factory, Azure Functions, and embedding pipelines for PDFs, Office documents, web content, and structured data.

  • Apply responsible AI, privacy, security, and compliance controls using Microsoft Entra ID, RBAC, managed identities, private networking, content safety, data loss prevention policies, prompt/response logging standards, and enterprise governance frameworks.

  • Monitor, evaluate, and optimize GenAI and agent performance using Azure Monitor, Application Insights, Azure AI Foundry evaluation capabilities, prompt testing, telemetry, quality metrics, latency tracking, token usage, and cost optimization practices.

  • Collaborate with solution architects, data engineers, AI/ML teams, security teams, and business stakeholders to translate GenAI opportunities into production-ready Azure implementations.

  • Leverage AI-assisted development and vibe coding practices using approved tools such as GitHub Copilot, Claude Code, AWS Kiro, Cursor, or equivalent enterprise-approved coding agents to accelerate requirements breakdown, solution design, code generation, refactoring, test creation, documentation, and pull-request preparation across the end-to-end SDLC.

  • Support CI/CD, infrastructure-as-code, environment promotion, release management, and operational readiness for Azure AI solutions using Azure DevOps, GitHub Actions, Terraform/Bicep, and approved enterprise deployment standards.

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