Forward Deployed Engineer, AWS Forward Deployed Engineering

A Forward Deployed Engineer II (FDE II) embeds directly inside a strategic enterprise customer to independently design build deploy and run AI-powered production systems alongside the customers engineering team. FDE IIs write production-grade software and own delivery of complete AI workstreams end-to-end from requirements gathering through production deployment combining the technical rigor of an AWS SDE with the urgency and ownership required to deliver measurable customer business outcomes.

FDE IIs are autonomous contributors. They deliver working features spanning the full software lifecycle. They design cohesive implementations for ambiguous problems making design trade-offs with appropriate guidance. They take on projects where the business problem is understood but implementations are not fully fleshed out balancing constraints and limiting short-term workarounds. They coach others on producing high-quality code and train new team members.

Key job responsibilities
Embed within customer engineering teams. Work with customers stakeholders and peers to understand the business and customer value of the systems you build. Understand the customers business processes technical architecture and operational constraints. Translate customer requirements into technical designs for AI-enabled production systems within your workstream. Requires 3050% travel.

Build production AI applications. Design and develop production-grade software powering AI and agentic workflows retrieval pipelines workflow automation API orchestration layers data integration. Solve difficult problems with visible risks or roadblocks bringing clarity and identifying simple designs for solutions. Integrate foundation models customer data sources and existing applications into cohesive AI experiences. Optimize for latency reliability and cost.

Own production for your workstream. Be a significant contributor to the full software development lifecycle including scoping design code testing deployment operations and maintenance. Troubleshoot production incidents across AI models distributed systems and application services. Ensure root causes of operational issues are identified and resolved. Implement monitoring evaluations guardrails and CI/CD that keep AI systems healthy. Dont settle for the status quo routinely identify and execute on opportunities to improve operations.

Mentor and uplift. Actively mentors and trains new team members on how systems are constructed operated and fit into the bigger picture. Coach others on producing high-quality code through mechanisms such as code review. Be active in the code review process providing meaningful feedback to others.

Accelerate customer adoption. Identify opportunities to expand AI adoption within your customer engagement. Codify reusable patterns and accelerators that benefit future customer deployments and feed back into AWS services. Make enhancements that improve your teams software and processes.

A day in the life
You start your morning reviewing deployment metrics from a recent release then join a design review where you help shape the architecture for an upcoming feature. After lunch you pair with a teammate on a tricky integration problem walking through trade-offs together before settling on a clean approach. Later you onboard a new team member by guiding them through how your services are constructed and operated. Your work balances building new capabilities with strengthening the foundations that keep your systems running reliably.

10047

- Experience in professional non-internship software development
- 3 years of non-internship professional software development experience
- 2 years of experience working in an Agile startup or rapidly scaling environment
- Experience communicating technical concepts to both technical and non-technical audiences
- Degree in Computer Science or related field or equivalent professional experience
- Experience with agentic and spec-driven development (developing AI agents a plus)
- Experience with AI tools: eval frameworks agent tooling RAG pipelines and prompt engineering

- Experience working in customer production environments
- Experience building or deploying AI/ML applications in production environments
- Background as a founder engineering lead or startup builder with high ownership and urgency
- Prior experience with React TypeScript Python and/or Go
- Experience interfacing with customers or leading technical projects with external stakeholders
- Experience working with teams that build and scale end-to-end production systems
- Experience with AWS services

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