Establish scalable operating models for partner engagement, technical scoping, architecture reviews, escalation, and delivery health
Set the technical bar for integrations, proofs of concept, reference architectures, and partner assessments produced by the team
Review architectures across AI applications, managed inference, cloud infrastructure, and data platforms, bringing in deeper experts where needed
Balance speed with engineering quality and ensure the team builds production-quality work rather than disposable demonstrations
Treat partner engagements as a continuous product discovery engine and convert field learning into roadmap priorities and shipped platform improvements
Ensure every engagement creates reusable value through platform capabilities, reference architectures, tooling, benchmarks, documentation, or automation
Prevent the function from becoming one-off consulting work by setting clear boundaries and rewarding reusable outcomes
Partner with Product and Engineering leadership to close the loop on recurring friction and improve the experience for customers beyond the original engagement
Strong relationships across the AI and cloud ecosystem
Public technical leadership through talks, workshops, reference architectures, open source projects, or developer advocacy
Built a Forward Deployed Engineering, Customer Engineering, or Field Engineering organization from zero to one
Scaled a technical field organization across regions or ecosystem domains without lowering the hiring or engineering bar
Experience as a founder, VP Engineering, Field CTO, Head of Customer Engineering, or early FDE leader
Led production architecture and execution for demanding AI application, training, inference, or platform workloads
Fast moving - Bold thinking - Constant growth - Meaningful impact - Trust and real ownership - Opportunity to shape the future of AI
Pay Transparency
Base Compensation Range: $270,800 - $310,000 USD