Google

Runs Search and YouTube and builds Gemini AI

Staff Partner Forward Deployed Engineer - GenAI, Google Cloud

AI Application EngineeringConfirmed 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.
Eight years of software development experience using Python or similar coding languages.
Experience architecting AI systems on cloud platforms such as Google Cloud Platform.
Experience building pipelines for structured and unstructured data using vector databases and RAG-like architectures for enterprise AI solutions.
Experience taking production-grade AI-driven solutions from conception to launch for customers.
Experience leading technical discovery sessions with customers.

Responsibilities

Serve as a team lead and developer within the strategic AI partner for complex AI applications, working with the partner’s teams to transition rapid prototypes into production-grade, replicable agentic workflows, including multi-agent systems and MCP servers, that drive measurable return on investment.
Build high-performance evaluation pipelines and observability frameworks to ensure partner-developed agentic systems meet requirements for accuracy, safety, and latency.
Identify repeatable partner and field patterns and friction points in Google’s AI stack, and convert them into reusable modules or formal product feature requests for Engineering teams.
Co-build with a strategic AI partner’s forward deployed engineering teams to establish Google-grade development best practices.
Help partners build their own agentic delivery capabilities for long-term success, focusing on return on investment at customer engagements and ensuring customer activation.

Desired Qualifications

A master's degree or PhD in AI, Computer Science, or a related technical field.
Experience implementing multi-agent systems using frameworks such as LangGraph, CrewAI, or ADK and complex patterns such as ReAct, self-reflection, or hierarchical delegation.
Knowledge of large language model-native metrics such as tokens per second and cost per request, and techniques for optimizing state management and granular tracing.

Serve as a team lead and developer within the strategic AI partner for complex AI applications, working with the partner’s own teams to transition from rapid prototypes to production-grade, replicable agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable return on investment (ROI).
Build high-performance evaluation pipelines and observability frameworks to ensure partner developed agentic systems meet rigorous requirements for accuracy, safety, and latency.
Identify repeatable partner and field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
Co-build with a strategic AI partner’s forward deployed engineering teams to instill Google-grade development best practices.
Help partners to build their own agentic delivery capabilities to set them up for long term success, focusing on the ROI at customer engagements ensuring customer activation.

Minimum qualifications:

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

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 Large Language Model 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.
8 years of experience with software development using Python or similar coding languages.
Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform).
Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
Experience taking production-grade AI-driven solutions from conception to launch for customers.
Experience leading technical discovery sessions with customers.

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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