AI Technical Enablement Lead, Forward Deployed Engineering

About the role

As part of the Google Cloud JAPAC AI Tech GTM team, the AI Technical Enablement Lead designs, runs and scales a high-touch technical onboarding program for Forward Deployed Engineers (FDEs) across the region. The role acts as the operational anchor for new hires, making sure they build deep product expertise, master customer engagement methods and finish rigorous skill-based capstones, while keeping the curriculum updated in real time to match the fast pace of AI technology and product change.

Key responsibilities

• Develop technical enablement programs based on Forward Deployed Engineer and Cloud AI CE requirements for learning AI products.
• Map persona-based views of technical GTM field enablement needs and lead the development of curriculum for in-person, e-learning and other assets.
• Design, build and run structured AI product training and enablement programs, and create materials for P0, P1 and other priority AI initiatives.
• Integrate into technical sales teams to understand enablement needs and become a trusted advisor to technical AI leadership.
• Crowdsource and coordinate subject matter experts across JAPAC and globally to build a sustainable framework that raises field readiness and drives customer impact.

Required profile

• Bachelor's degree in Engineering, Computer Science or a related field, or equivalent practical experience.
• 5 years of experience developing software with Python or similar languages.
• Experience taking production-grade AI solutions from concept to launch and architecting AI systems on cloud platforms such as GCP.
• Experience building pipelines for structured and unstructured data using vector databases and RAG-like architectures for enterprise AI.
• Experience leading technical discovery sessions.
• Preferred: Master's or PhD in AI, Computer Science or a related field; experience with multi-agent systems (LangGraph, CrewAI, ADK) and patterns such as ReAct, self-reflection and hierarchical delegation; knowledge of LLM-native metrics like tokens per second and cost per request.