• AI Platform Integration: Build robust Elixir services that consume ML predictions from Modal.com, Vertex AI, and other ML platforms • Integrate existing (or new!) products with edge-based inference where appropriate • Create fault-tolerant systems that gracefully handle service failures and provide intelligent fallbacks • Implement caching and optimization strategies for AI feature serving • Build real-time data pipelines using Kafka and Elixir processes to feed ML models • Production AI Features: Develop Demand Planning services that integrate forecasting models with inventory management • Build intelligent routing systems that leverage ML predictions for optimal logistics decisions • Create real-time anomaly detection services for supply chain monitoring • Implement AI-powered recommendation engines for logistics optimization • Design and build APIs for LLM integration (OpenAI, Anthropic, Workers AI, etc.) with proper rate limiting and error handling • Platform Architecture & Scalability: Design distributed systems that can handle millions of AI-powered logistics operations • Implement proper monitoring, alerting, and observability • Build developer tools and abstractions that make AI capabilities easy for other engineers to integrate • Create configuration management and feature flagging for experiments • Optimize performance and costs across the platform • Cross-functional Collaboration: Partner with the Dir of AI Products on architecture decisions and technical strategy • Collaborate with Data Scientists to understand model inputs, outputs, and constraints • Work with the ML/Data Engineers to ensure seamless model deployment integration • Support product teams in implementing AI features within their domains • Mentor other engineers on AI tool usage and integration patterns and best practices