Google

Runs Search and YouTube and builds Gemini AI

Forward Deployed Engineer - Generative AI, 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.
At least 5 years of software development experience using Python or a similar coding language.
Experience architecting AI systems on cloud platforms such as Google Cloud Platform.
Experience building structured and unstructured data pipelines using vector databases and retrieval-augmented generation architectures for enterprise AI solutions.
Experience taking production-grade AI-driven solutions from conception to launch for customers.

Responsibilities

Develop complex AI applications, moving rapid prototypes into production-grade agentic workflows, including multi-agent systems and Model Context Protocol servers, that deliver measurable return on investment.
Architect and code integrations between Google's AI products and customers' live infrastructure, including APIs, legacy data silos, and security perimeters.
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
Identify repeatable 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 customer engineering teams to instill Google's development best practices and support long-term project success and high end-user adoption.

Desired Qualifications

Experience implementing multi-agent systems with frameworks such as LangGraph, CrewAI, or ADK, and patterns such as ReAct, self-reflection, or hierarchical delegation.
Experience leading technical discovery sessions.
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.
A Master's degree or PhD in AI, Computer Science, or a related technical field.

Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable return on investment.
Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters as part of an expert team.
Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
Identify repeatable field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for engineering teams.
Be able to co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

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 a similar coding languages.
Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
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.

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).
Experience leading technical discovery sessions.
Knowledge of Large Language Model (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 a similar coding languages.
Experience architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
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.

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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