Power Platform Architect - 100% Remote in United

Energy Jobline
United, United States
7 days ago
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Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Shift work

Tech stack

Application Programming Interfaces (APIs) Microsoft Azure Continuous Integration Python (Programming Language) Named Entity Recognition Microsoft SharePoint TypeScript Microsoft Power Automate Power Platform Integration Retrieval-Augmented Generation Large Language Models Prompt Engineering
+6 more
Backend Microsoft Copilot Studio Graphql Api Management Serverless Computing Powerapps

Job description

We are seeking an experienced Microsoft Copilot Studio Power Platform Architect with strong hands on expertise across Copilot Studio Power Automate Power Apps Dataverse and enterprise grade integrations The ideal candidate will design and develop intelligent workflows copilots and automation solutions leveraging modern LLM capabilities prompt engineering and Graph API integrations, * Build extend and maintain copilots using Microsoft Copilot Studio including topics entity extraction actions and generative answers \n

  • Develop custom extensions using Power Automate custom connectors and APIs \n

  • Integrate copilots with enterprise data sources such as Dataverse Graph API SharePoint Azure Functions and external services \n, Experience integrating backend systems using connectors GRAPH APIs Azure Functions Dataverse 3rd party

Requirements

  • Experience in building LLM appsagents \n

  • Hands on with Azure AI Foundry project hubs deployments prompt flows evaluations \n

  • Strong in Python TypeScript for agent logic function calling and API integrations \n

  • Solid understanding of LLM prompting system prompts fewshot patterns and safety guardrails \n

  • Experience with Azure services AI Search OpenAIOSS model deployments Functions Storage Key Vault Event GridService Bus \n

  • Knowledge of RAG design chunking strategies embeddings hybrid search rerankers \n

  • Proficient in CICD IaC observability and cost optimization for LLM workloads \n

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