Senior Forward Deployed Engineer, AWS Forward Deployed Engineering

A 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. 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.

A 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. 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.

• 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 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.
• Raise the bar. Mentor engineers. Contribute to AWS engineering best practices for AI and forward deployment. Push reusable components and learnings back into AWS products and services.

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