Sr. AI Engineer

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 )
• 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

Configure And Deploy Azure AI & Microsoft Ecosystem Implementation

• Azure OpenAI / model endpoints
• Azure AI Search (vector + semantic retrieval)
• Application Insights / Log Analytics monitoring
• Build and support Copilot Studio environments and orchestration layers
• Azure AI Foundry‑based custom agent services

Co‑Development & Enablement

Work directly with consultants and internal teams to:

• Co‑build pilot agents
• Facilitate adoption and value‑based outcomes

Partner With Data Engineering Team On

• Data product and semantic layer alignment
• AI orchestration
• Observability and feedback loops

Enable Internal Teams On

• Agent design patterns
• Responsible AI practices
• Ongoing support and scaling

What You'll Bring Technical Expertise Strong Experience With

• Azure AI services (Foundry, Cognitive Services, AI Search)
• Microsoft Copilot Studio / Power Platform
• Cloud‑native architecture (Azure)

Experience Building

• Conversational AI / chatbot / agent solutions
• RAG pipelines and LLM-based applications
• API integrations, MCP frameworks, and enterprise workflows

Architecture & Engineering

Proven ability to design:

• Scalable, secure AI platforms
• Hybrid architectures (low‑code + pro‑code)

Experience With

• MLOps and CI/CD pipelines
• Monitoring and observability (App Insights, logging, tracing)
• Secure cloud networking and identity
• Validating and optimizing AI systems
• Evaluating and selecting AI platforms and tools (cloud‑native and third‑party)
• Defining design patterns, standards, and guardrails for AI solutions
• Balancing rapid experimentation with maintaining platform consistency and avoiding fragmentation

Experience Implementing Governance & Responsible AI

• AI governance frameworks
• Data security, privacy and compliance controls
• Lifecycle management and deployment gating
• Understanding of regulatory considerations (e.g., data privacy, AI compliance risks)

Experience with auditing, monitoring, and incident response for AI systems in production.

Experience

• 5+ years in cloud / data / AI engineering or architecture roles
• 5+ years in software development or data engineering
• Hands‑on experience building LLM‑based applications or…