Applied AI Engineer (Supply Chain)

• The Supply Chain AI & Data team drives the strategic transformation of our operations in alignment with Vision 2030. We are building the next evolution of data on AI—moving beyond traditional analytics to architect an intelligent, autonomous supply chain ecosystem
• As the strategic engine behind critical initiatives, our mission is to fundamentally redefine how the business operates, turning complex logistical challenges into proactive, resilient, and forward-looking capabilities
• As an Applied AI Engineer I on our Supply Chain team, you will help bridge the gap between cutting-edge artificial intelligence and real-world logistical challenges
• You will work closely with senior engineers, data scientists, and supply chain operators to build and deploy intelligent systems that optimize our operations
• A major focus of this role will be developing Conversational AI interfaces for our supply chain data, and subsequently evolving those tools into proactive alerting systems and semi/fully autonomous AI agents that can execute decisions in real time
• Moving from foundational support to owning specific project components, you will play a hands-on role in making our supply chain smarter, faster, and more resilient
• Conversational AI for Data: Build and maintain Large Language Model (LLM) applications (such as Text-to-SQL or RAG systems) that allow stakeholders to easily query ERP, warehouse, and logistics data using natural language
• Proactive Alerting Systems: Expand conversational tools into active monitoring systems that continuously analyze supply chain data streams, detect anomalies (e.g., inventory shortages, shipping delays), and automatically push alerts and actionable recommendations to operators before bottlenecks occur
• Autonomous & Semi-Autonomous Agents: Develop and deploy agentic AI workflows that can take independent action—such as rerouting shipments, generating purchase orders, or automating vendor communications—while designing secure Human-in-the-Loop (HITL) fallback mechanisms
• Model Development & Deployment: Collaborate on designing, training, and deploying standard machine learning models for supply chain optimization (e.g., route planning, demand forecasting)
• Data Pipeline Engineering: Write clean, production-ready code to extract, clean, and preprocess large datasets from our core operational systems to feed both predictive models and LLMs
• Cross-Functional Collaboration: Partner with supply chain analysts and domain experts to translate operational pain points into actionable, agentic AI solutions
• Position Type: New Grad

Benefits

• Flexible work models
• Paid time off
• Paid parental and family leave
• Health care benefits
• Global wellness resources
• Employee assistance programs
• Rotation programs
• Mentor relationships
• Learning and development opportunities
• Retirement benefits
• Employee bonuses
• Stock grants & employee stock purchase plans- Problem Solving: A strong analytical mindset with a genuine curiosity about how goods move and how systems can be optimized through autonomous workflows
• AI/ML & Generative AI: Hands-on experience (through internships, academic projects, or previous roles) with LLMs, prompt engineering, and agentic AI frameworks (e.g., LangChain, LangGraph, AutoGen, or CrewAI)
• Database Skills: Solid understanding of SQL and experience querying relational databases to support conversational data retrieval
• Programming: Strong proficiency in Python and standard data manipulation libraries (e.g., Pandas, NumPy)
• Communication: Ability to communicate technical concepts clearly to non-technical supply chain stakeholders
• Bachelor’s or Master’s degree in Computer Science, Data Science, Operations Research, Engineering, or a related quantitative field
• Cloud Platforms: Experience with cloud computing environments (AWS, GCP, or Azure) and their respective AI/ML services
• Domain Knowledge: Basic understanding of supply chain management, logistics, procurement, or manufacturing concepts
• Event-Driven Architecture: Exposure to data streaming, webhooks, or event-driven systems to support real-time proactive alerting
• Software Engineering Best Practices: Familiarity with version control (Git), containerization (Docker), API development, and CI/CD pipelines