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Sr. AI Engineer

Packsize
Salt Lake City, United Statesfull_timeVerifiedPosted 26 Jun 2026
💰 $180,000/yr($140,000/yr$180,000/yr)

About the role

Job Description:

Sr. AI Engineer

Preferred Locations: Salt Lake City, UT; Louisville, KY, or Amsterdam (All Hybrid)

About Packsize 

Packsize is redefining the way businesses and their customers use and experience packaging around the world. We build the technology, design the right solutions, and automate the processes that propel the industry forward. To us, packaging is much more than a box—it’s delivering what’s right for our customers, their customers, our people, and the planet. 

About the Role

We are seeking an experienced AI Engineer to partner with our internal Data & Analytics and IT teams to design, build, and operationalize production-grade AI agents within the Microsoft ecosystem. 

This role will focus on delivering enterprise-ready solutions using: 

  • Microsoft Copilot Studio (low-code orchestration) 

  • Azure AI Foundry (custom agent development & advanced processing) 
     

The engagement will operate in a co-building model, working alongside consultants and internal teams to deliver initial AI pilot agents while establishing a scalable, governed AI platform. 

What You'll Do:

AI Agent Architecture & Design:

​Define reference architecture for agentic AI solutions across Copilot Studio and Azure AI Foundry 

Establish design patterns for:  

  • Retrieval-Augmented Generation (RAG) 

  • Multi-agent orchestration 

  • Enterprise integrations (SAP, Salesforce, Databricks, SharePoint, Azure Ecosystem) 

Guide use-case prioritization and platform selection (Studio vs Foundry vs hybrid)   

 

AI Agent Development & Delivery: 

Build and deploy production-grade AI agents, including:  

  • Knowledge & troubleshooting agents 

  • Operational / workflow automation agents 

  • Data and analytics-driven agents 

-Implement:  

  • Prompt engineering and evaluation strategies 

  • Agent workflows and orchestration logic 

  • API, connector, and system integrations 

 

Platform Foundation & Governance: 

Establish enterprise AI guardrails, including:  

  • Security, RBAC, and identity integration (Entra ID) 

  • Data access boundaries and governance 

  • Audit logging and monitoring frameworks 

-Define and implement:  

  • Agent lifecycle (draft → pilot → production → retirement) 

  • CI/CD pipelines and deployment standards 

 

Azure AI & Microsoft Ecosystem Implementation:

Configure and deploy:  

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Company

Packsize

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