AI Technical Enablement Lead, Forward Deployed Engineering

• Develop deep technical enablement programs based off of Forward Deployed Engineer and Cloud AI CE requirements for learning of AI products.
• Map persona-based views of technical GTM field enablement needs, leading the development of curriculum for in-person, e-learning and other assets.
• Design, build and execute structured AI product deep technical training and enablement programs, and build-out of 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.

Minimum qualifications:

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 5 years of experience with software development using Python or similar coding languages.
• Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., GCP).
• Experience building pipelines for structured and unstructured data using both vector databases and Retrieval-Augmented Generation (RAG)-like architectures to power enterprise AI solutions.
• Experience leading technical discovery sessions.

Preferred qualifications:

• Master’s degree or PhD in AI, Computer Science, or a related technical field.
• Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
• Knowledge of "LLM-native" metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 5 years of experience with software development using Python or similar coding languages.
• Experience taking production-grade AI-driven solutions from conception to launch and architecting AI systems on cloud platforms (e.g., GCP).
• Experience building pipelines for structured and unstructured data using both vector databases and Retrieval-Augmented Generation (RAG)-like architectures to power enterprise AI solutions.
• Experience leading technical discovery sessions.