Teams at AZX

Build and deploy production machine-learning systems within client environments, covering data discovery, modeling, validation, deployment, scheduling, monitoring, and retraining. Develop forecasting and detection models for imperfect time-series data, establish honest evaluation and KPI frameworks, and ship usable APIs or interfaces. Work directly with client IT and data teams through discovery, demos, and feedback cycles. The role also involves data engineering, software development, basic cloud and DevOps work, and advising clients when ML is not appropriate.

Own end-to-end development of AI product demos, including React and TypeScript interfaces, streaming chat and agent workspaces, backend-for-frontend services, database schemas, deployment, observability, and on-call. Partner on app-builder tooling, design systems, agent-facing CLI and deployment experiences, accessibility, localization, audit trails, and enterprise readiness. Debug across backend and Kubernetes infrastructure while delivering reliable, AI-native product experiences.

Own the AI inference and runtime platform, including open-weight model serving, Kubernetes operators and autoscaling, GPU infrastructure, stateful vector and graph stores, and secure microVM-based agent sandboxing. Build Rust, Python, and Kubernetes control-plane services; manage infrastructure as code, observability, metering, lifecycle operations, upgrades, backups, and failover. Establish security controls, open-source standards, and reliable production operations across cloud and managed GPU environments.