Principal Engineer

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!

Meijer Rewards

• Weekly pay

• Scheduling flexibility

• Paid parental leave 

• Paid education assistance

• Team member discount

• Development programs for advancement and career growth

Please review the job profile below and apply today!

What You Will Be Doing 

• Defines and maintains the reference architecture for the enterprise AI platform and ensures solutions align with it. 

• Leads the evaluation and selection of platform technologies, including API gateway, service catalog, identity, evaluation and observability components, against documented criteria. 

• Establishes engineering standards for packaging, deploying, securing and monitoring AI capabilities across cloud and on-premises environments. 

• Leads major platform initiatives, including the modernization of existing AI applications, with attention to continuity of service and preservation of existing safety controls. 

• Owns problem resolution across the team's services: identifies recurring and systemic issues from incident trends, leads root cause analysis for major or cross-domain incidents, and drives architectural and process remediation so problems do not recur. 

• Sets the standards for incident severity, on-call practice and post-incident review across the team. 

• Sets the technical standard for the engineering team through design review, mentoring and written guidance. 

• Leads the technical dimension of vendor relationships and platform evaluations in partnership with finance and information security. 

• Advises technology leadership on architecture decisions, emerging technologies, risks and trade-offs. 

• Remains hands-on: produces the reference architecture, prototypes, evaluation criteria and written standards as concrete deliverables, not only guidance. 

• Designs, develops, tests and deploys application features and services, including integrations with enterprise systems and data sources. 

• Builds AI-enabled application capabilities, including retrieval components, tool and API integrations, and automated evaluation of AI-generated responses. 

• Builds and deploys containerized services through CI/CD pipelines and operates them in production, including monitoring, alerting and performance tuning. 

• Writes and maintains automated tests; monitors quality and takes corrective action. 

• Participates in the on-call rotation, responds to incidents and contributes to root cause analysis and corrective actions. 

• Uses AI-assisted development tools in daily work and shares effective practices with the team. 

• Writes clear technical documentation, including designs, runbooks and operational procedures. 

• Collaborates with product owners, analysts, security and partner teams on requirements, acceptance criteria and solutions. 

• Participates in Agile team ceremonies and in planning and prioritization. 

What You Bring With You (Qualifications) 

• Bachelor's degree in Computer Science, Information Systems or a related field, or equivalent experience. 

• 5+ years of professional experience building and operating platforms used by other engineering teams, including ownership of an enterprise-scale architecture. 

• Experience building or operating an AI or machine learning platform in production, such as model serving and inference, retrieval systems, evaluation frameworks, agent runtimes or AI governance tooling, including experience with machine learning systems that predates generative AI. 

• Experience designing for hybrid cloud and on-premises deployment, with the ability to articulate cost and performance trade-offs. 

• Experience leading incremental modernization of live production systems. 

• Experience in a regulated or high-trust environment, with working knowledge of identity, access control, auditability and incident response. 

• Excellent written and verbal communication skills, including architecture documentation for technical and executive audiences. 

• Demonstrated use of AI tools in daily work, such as AI-assisted coding, testing, documentation or analysis, with the ability to describe what has worked and what has not. 

Preferred Qualifications 

• Depth in a cloud AI platform such as Azure AI Foundry, Amazon Bedrock or Google Vertex AI. 

• Experience with the Model Context Protocol ecosystem and with serving open-weight models. 

• Knowledge of data platforms such as Databricks. 

• Knowledge of ServiceNow as an enterprise integration, workflow and service management platform. 

• Retail or frontline-workforce domain experience. 

• Experience with Agile methodologies at scale.