AI Engineer

United States, North Carolina, Charlotte

Oct 27, 2026

SEE Headquarters - Charlotte

Sealed Air designs and delivers packaging solutions that protect essential goods transported worldwide, preserve food, enable e-commerce and digital connectivity, and help create a global supply chain that is touchless, safer, less wasteful, and more resilient. We strive to foster a caring, high-performance growth culture that will deliver consistent, sustainable profitable growth and accelerate our performance - a culture where accountability is clear and aligned, and where we reward business outcomes and impact.

That culture guides everything we do, including how we partner with our customers and suppliers, how we attract and retain top talent, and how we create and deliver value to our stakeholders. In 2025, Sealed Air generated $5.4 billion in sales and has approximately 16,100 employees who serve customers in 119 countries/territories. To learn more, visit

Sealed Air is seeking a hands‑on Senior AI Engineer to design, develop, deploy, and support AI‑enabled applications for supply chain and manufacturing operations. The role combines software engineering and solution architecture with predictive machine learning, industrial IoT, MES integration, and generative AI to improve equipment reliability, work centre performance, product quality, and operational productivity.

The engineer will partner with supply chain leaders, plant operations, maintenance, OT, data engineering, and IT teams to turn business problems into secure, reliable production solutions and measurable outcomes.

• Manufacturing AI and predictive analytics. Develop and operationalize ML solutions for predictive maintenance, anomaly detection, equipment failure risk, cycle time and throughput forecasting, quality prediction, and work centre bottleneck analysis. Define baselines and measure operational impact with plant stakeholders.
• Industrial data and MES integration. Integrate machine and sensor telemetry, historian data, MES events, maintenance records, and ERP data. Connect equipment signals with production orders, assets, work centres, downtime reasons, and quality results to create meaningful analytical features.
• Model engineering and lifecycle. Build reproducible data preparation, training, evaluation, deployment, and retraining pipelines. Address time‑series alignment, missing data, data leakage, class imbalance, and changing operating conditions; monitor model drift, false alarms, and prediction quality.
• LLM and RAG applications. Build applications using LLMs, embeddings, vector search, and retrieval‑augmented generation (RAG) for manufacturing knowledge, troubleshooting, and operational assistance. Evaluate retrieval quality, grounded responses, access controls, latency, and cost.
• AI automation. Develop AI‑assisted workflows that turn predictions and retrieved knowledge into alerts, recommendations, maintenance requests, and approved business actions. Apply human review, auditability, and appropriate OT controls to actions affecting plant operations.
• Full stack engineering and Azure operations. Design and build APIs, backend services, databases, and web interfaces. Deploy, configure, monitor, troubleshoot, and support applications in Azure App Service, including identity, secrets, networking dependencies, availability, and application performance.
• Dev Ops and production ownership. Use Azure Dev Ops for source control, work tracking, pull requests, automated testing, and CI/CD across development, test, and production. Maintain deployment approvals, rollback procedures, monitoring, incident diagnostics, and support documentation.
• Technical leadership…