Senior Forward Deployed Engineer, AWS Forward Deployed Engineering

AWS has formed a new Forward Deployed Engineer (FDE) team dedicated to embedding AI Engineers and Scientists directly inside strategic enterprise customer environments to help design, build, and deploy AI-powered production systems. We don’t build from a distance—we sit with customers, work in their infrastructure, and deliver production-grade AI solutions that transform how they operate. This initiative is scaling rapidly. The Senior Forward Deployed Engineer (FDE) embeds directly inside a strategic enterprise customer to design, build, deploy, and run AI-powered production systems alongside the customer's engineering team. Senior FDEs write production-grade software and own outcomes end-to-end, from prototype through enterprise-scale deployment combining the technical rigor of an AWS SDE with the urgency and ownership required to deliver measurable customer business outcomes. They create reusable architectures, patterns, and engineering mechanisms that scale the entire FDE practice. Key job responsibilities • Embed within customer engineering teams. Understand the customer's business processes, technical architecture, and operational constraints. Translate ambiguous business problems into scalable AI-enabled production systems. Requires up to 30 – 50% travel. • Build production AI applications. Design and develop production-grade software powering AI and agentic workflows — orchestration layers for multi-agent systems, retrieval pipelines, workflow automation, decision intelligence. Integrate foundation models, customer data sources, APIs, and existing applications into cohesive AI experiences. Optimize for latency, reliability, observability, cost, and security. • Own production. Take systems from design through production rollout and operationalization. Troubleshoot production incidents across AI models, distributed systems, data pipelines, and application services. Put in place monitoring, evaluations, guardrails, and the operational mechanisms (testing, CI/CD, rollback, resiliency) that keep AI systems healthy at scale. • Accelerate customer transformation. Identify opportunities to expand AI adoption across customer workflows. Codify reusable patterns, accelerators, and reference architectures that benefit future customers and feed back into AWS. • Build and maintain internal AI tools and customer-facing assets and AI-enbled products to accelerate and scale the FDE motion. • Route field signal into the closed-loop product feedback mechanism such as filing PFRs, escalating deployment-critical gaps, and feeding delivery learnings that become the next AWS primitives (as we already do with AWS Transform and Kiro). • Raise the bar. Mentor engineers. Contribute to AWS engineering best practices for AI and forward deployment.