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Lead client engagements involving AI and machine learning solutions. Translate customer outcomes into technical direction, define requirements and success measures, review architectures and data pipelines, coordinate engineering delivery, and manage project planning, budgets, forecasts, and workstreams. The role requires technical fluency across APIs, cloud platforms, CI/CD, and ML deployment, plus experience delivering custom software and data solutions for complex, multi-stakeholder customers.

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.

Build and maintain backend infrastructure for AZX’s LLM gateway, including routing, rate limiting, key management, observability, sandboxing, Kubernetes services, operators, and autoscaling. Develop high-performance services in Go, Rust, or async Python using Envoy and gRPC. Support reliable model serving across cloud and customer-managed clusters, collaborate across platform teams, and help develop scalable inference and evaluation capabilities.

Build and deploy AI solutions for clients, including agentic workflows, RAG systems, structured extraction, OCR and document-processing pipelines, retrieval systems, evaluation harnesses, and lightweight interfaces. Translate ambiguous client problems into production code, drive engagements from discovery through deployment, collaborate with stakeholders, and contribute to internal platform capabilities and engineering practices.