Google

Runs Search and YouTube and builds Gemini AI

AI Technical Enablement Lead - Forward Deployed Engineering

Instructional Design & Curriculum DevelopmentConfirmed live in the last 24 hours

Full-timeSingaporeNo salary listedIn-person

About the Job

Requirements

A bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience.
Five 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 such as GCP.
Experience building pipelines for structured and unstructured data using vector databases and Retrieval-Augmented Generation-like architectures to power enterprise AI solutions.
Experience leading technical discovery sessions.
A master's degree or PhD in AI, Computer Science, or a related technical field is preferred.
Experience implementing multi-agent systems using frameworks such as LangGraph, CrewAI, or ADK and complex patterns such as ReAct, self-reflection, and hierarchical delegation is preferred.
Knowledge of LLM-native metrics such as tokens per second and cost per request, and techniques for optimizing state management and granular tracing is preferred.

Responsibilities

Develop deep technical enablement programs based on Forward Deployed Engineer and Cloud AI CE requirements for learning AI products.
Map persona-based views of technical go-to-market field enablement needs and lead 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 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.

Desired Qualifications

Experience implementing multi-agent systems using frameworks such as LangGraph, CrewAI, or ADK and complex patterns such as ReAct, self-reflection, and hierarchical delegation.
Knowledge of LLM-native metrics such as tokens per second and cost per request, and techniques for optimizing state management and granular tracing.

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.

About the Company

Google

Runs Search and YouTube and builds Gemini AI

Google runs the world's most used search engine and free services such as YouTube, Android, and Gmail, and it builds the Gemini AI models that work inside them. In 2017, its researchers invented the transformer, the neural network design behind ChatGPT and most other AI chatbots. People use these services without paying, and Google earns most of its money from ads placed beside search results and YouTube videos. Businesses rent computing power and Gemini models through Google Cloud, and Google also sells subscriptions and Pixel phones. Larry Page and Sergey Brin founded Google in 1998 as Stanford PhD students; it is based in Mountain View, California, United States, and is the main business of parent company Alphabet.

Company size: 10,001+
Company stage: IPO
Headquarters: Mountain View, CA
Founded: 1998

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