Principal Engineer

Meijer•Grand Rapids, MI

4d•Hybrid

About The Position

As a family company, we serve people and communities. When you work at Meijer, you’re provided with career and community opportunities centered around leadership, personal growth and development. Consider joining our family – take care of your career and your community! The role will identify, analyze, and support business drivers, technology strategies, and current technology environments to recommend solutions to improve, develop, and align engineering to the specific domain. The position will lead, consult or oversee multiple highly complex projects/programs/domains that have significant impacts and require in-depth technical knowledge across multiple specific architecture disciplines such as technology, solution, business, or information/data. Position follows our hybrid schedule: Monday-Wednesday in Grand Rapids MI corporate office, Thursday-Friday remote.

Requirements

Bachelor’s (or comparable combination of education and related experience).
5+ years of progressive responsibility in an IT environment with demonstrated technical knowledge which provides the necessary skills, knowledge, and abilities.
Advanced knowledge of software development best practices.
Exposure to a wide variety of software project management methodologies and frameworks with a deep understanding of agile development methodologies.
Successfully developed and implemented applications in new computing architecture environments using new and emerging technologies.
Developed efficient and effective solutions to diverse and complex business problems
Technical team leadership experience.
Experience with multiple, diverse technical configurations, technologies, and processing environments.
Experience with structured design, development and implementation of new and established computing architectures.

Nice To Haves

Experience in the retail industry or in a production/service environment is preferred.
Experience in Scaled Agile framework is preferred.
Enterprise-scale experience architecting AI or GenAI solutions on the Microsoft Azure stack (Azure OpenAI, Azure AI Foundry, AKS, Azure Functions, Azure DevOps), including RAG, agentic and multi-agent orchestration, Model Context Protocol (MCP), and LLM evaluation frameworks, is preferred.
Working knowledge of LLMOps, responsible AI governance (model risk, content safety, data privacy), and cost/capacity management (PTUs, quotas, model routing) for production AI platforms is preferred.
Experience guiding teams through AI-assisted and spec-driven development, context/prompt engineering standards, and evaluating GenAI frameworks such as LangChain/LangGraph, Semantic Kernel, or AutoGen is preferred.

Responsibilities

Architect, design, and review implementation details for a specific domain within the company’s AI solutions.
Designs, codes, and implements new industry leading design patterns in domain specific code bases, to create the convention/technique/practice that teams are accountable to follow.
Mentors specific domain engineering teams on principles and best practices.
Provides engineering strategy to ensure code modernization and industry standard patterns for specific domain.
Builds proof of concepts (POCs) that are production ready.
Provides guidance to development teams within domain requirements.
Leads non-functional requirements (NFRs) such as security, reliability, performance, maintainability, scalability, and usability related to the specific domain.
Promotes the implementation of new technology, solutions, and methods to improve business processes, efficiency, effectiveness, and value delivered to customers.
Defines and evolves reference architectures for AI & Automation workloads on the Microsoft Azure stack — including Azure OpenAI, Azure AI Foundry, Retrieval-Augmented Generation (RAG) at scale, agentic and multi-agent orchestration, Model Context Protocol (MCP) tool integration, and Microsoft Copilot Studio — ensuring alignment with Enterprise Architecture and security standards.
Guides LLMOps and responsible AI practices across domains — including evaluation harnesses, guardrails and content safety, model routing and fallback, PTU/quota capacity planning, and cost optimization — and leads architectural reviews of AI/GenAI solutions prior to production deployment.