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.

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.