Forward-Deployed AI Systems Engineer

Amazon; Arlington (VA); On-site; USD 144,000 - 194,000; Full time

Job summary

Amazon is seeking a Forward Deployed Engineer II who embeds with enterprise customers to design, build, deploy, and run AI-powered production systems alongside their engineering teams. The role covers full software lifecycle and requires significant travel (30–50%).

You will implement cohesive AI experiences, optimize for latency and cost, and own the production lifecycle for your workstream. You will mentor teams, drive high-quality code, and accelerate AI adoption by creating reusable patterns

Qualifications

• 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

Responsibilities

• Embed within customer engineering teams and translate customer requirements into technical designs for AI-enabled production systems
• Build production-grade software powering AI and agentic workflows with attention to latency, reliability, and cost
• Own production for your workstream across scoping, design, code, testing, deployment, operations, and maintenance
• Mentor and uplift teammates through code reviews and training on system design and operations
• Accelerate customer adoption by codifying reusable patterns and accelerators for future deployments

Job description

Amazon is seeking a Forward Deployed Engineer II who embeds with enterprise customers to design, build, deploy, and run AI-powered production systems alongside their engineering teams. The role covers full software lifecycle and requires significant travel (30–50%).

You will implement cohesive AI experiences, optimize for latency and cost, and own the production lifecycle for your workstream. You will mentor teams, drive high-quality code, and accelerate AI adoption by creating reusable patterns