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 customer's 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 customer's business processes, technical architecture, and operational constraints.
Translate customer requirements into technical designs for AI-enabled production systems within your workstream.
Requires 30–50% 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.
Don't 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 team's software and processes.