Senior Engineering Manager, AWS

- 10+ years of non-internship professional software development experience
- 5+ years of engineering organization management experience (managing managers or large teams)
- 8+ years of leading design or architecture of new and existing systems
- Experience defining technical strategy and delivery standards for an engineering organization
- Experience managing teams across multiple concurrent programs or product lines
- Experience managing engineering teams in customer-facing or forward-deployed environments at scale
- Experience building or leading organizations delivering production AI or ML platforms
- Track record of developing engineering managers and senior technical leaders
- Experience representing engineering to executive leadership (VP/C-suite level)
- Strong written and verbal communication skills; comfort working in customer-facing environments
- Experience influencing product roadmaps through engineering and customer insights
- Bachelor's degree in computer science or equivalent; advanced degree preferred

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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.

A Senior Forward Deployed Engineering Manager (Senior FDE Manager) leads multiple pods of FDEs across several strategic enterprise customers, defining the delivery model, engineering standards, and talent strategy for the FDE practice at scale. This is not a Solutions Architect management role and not a consulting engagement manager role. Senior FDE Managers own organizational-level outcomes — they set the technical direction across multiple concurrent customer programs, build and scale the FDE team, and drive the mechanisms that ensure consistent engineering excellence and measurable business impact across the entire portfolio.

Senior FDE Managers operate at org-level scope. They lead 15–30+ engineers, influence technical decisions across multiple customer engagements and AWS teams simultaneously, and define what "great" looks like for forward-deployed AI engineering management. They represent the FDE practice to AWS senior leadership and customer C-suite executives, balancing strategic vision with operational execution.

Key job responsibilities
• Lead the FDE delivery organization. Own delivery across multiple customer engagements and FDE pods. Staff and structure teams for optimal customer coverage. Define engagement models, staffing strategies, and delivery cadences. Ensure consistent engineering quality across all active programs. Requires up to 30 – 50% travel.

• Define technical strategy and standards. Set the technical direction for the most complex customer AI programs spanning your organization. Align technical strategy with adjacent AWS teams. Be a key influencer for strategic decisions, bringing technology and industry context to business decisions. Resolve escalations and drive closure with peers, Directors, and stakeholders.

• Own production excellence at organizational scale. Define operational standards, evaluation frameworks, and production readiness criteria for AI systems across your organization. Establish mechanisms that ensure AI systems remain healthy across all active engagements. Drive improvements that enable the organization to insist on the highest operational excellence standards.

• Build and scale the team. Hire, develop, and retain FDE talent across levels. Coach and develop FDE Managers. Drive performance management, succession planning, and organizational health. Create the talent pipeline for the FDE practice. Define hiring bar and calibration standards.

• Accelerate transformation at portfolio level. Identify systemic opportunities to expand AI adoption across multiple customer organizations. Create accelerators and reusable platforms that reduce time-to-value. Influence AWS product roadmaps based on patterns observed across customers. Feed learnings back into AWS at the organizational level.