New opportunity

Forward Deployed Engineer Intern, 2027

Google · Singapore

About this role

The Forward Deployed Engineer Intern role within the Google Cloud Consulting team is a 10-12 week paid internship focused on bridging the gap between frontier AI products and production-grade enterprise solutions. You will act as an embedded builder, working directly within customer environments to code, debug, and ship bespoke agentic solutions. Responsibilities include designing connective tissue such as APIs and data pipelines, managing project deliverables, and providing critical feedback to shape Google Cloud's product roadmap. You will collaborate with customer engineering teams to implement best practices and solve complex integration and state-management challenges. Minimum qualifications include pursuing a Bachelor's or Master's degree in Computer Science or a related field, being in the penultimate year of study, and having experience with software development and data pipelines or AI/ML workflows. Preferred qualifications include experience with multi-agent orchestration frameworks like LangGraph or CrewAI, knowledge of LLM-native metrics, and backend system development experience. Candidates must be located in Singapore for the duration of the internship and possess strong English proficiency. Google prioritizes applicants with a current right to work in Singapore.

For full details about this role, click “Apply on company site” to view the employer’s original posting.

Skills for this role

PythonCC++JavaJavaScriptGoRustData PipelinesCloud DatabasesAI/ML WorkflowsRAGMulti-agent OrchestrationLangGraphCrewAIADKState ManagementGranular TracingLLM Metrics

Checking your profile…

Preparation suggestions, not the employer’s actual interview questions. Always check the original posting for current requirements.

Understand the role

What matters most

Production-Grade Agentic Development
The core mandate is to transition AI prototypes into robust, enterprise-ready solutions. You must demonstrate the ability to write clean, maintainable code that handles real-world complexity, including state management, API integration, and security perimeters, rather than just building high-level architectural concepts.
Customer-Facing Technical Collaboration
As an embedded builder, you must act as a bridge between Google and the customer. This requires strong technical communication skills to lead discovery workshops, instill development best practices within customer teams, and translate field-level technical barriers into actionable product feedback for Google's core engineering teams.
System Integration and Data Engineering
You are responsible for designing the connective tissue between Google Cloud AI products and existing enterprise infrastructure. This involves building and executing data pipelines and ensuring seamless integration with cloud databases, requiring a solid grasp of backend system development and data flow management.

Likely challenges to think through

Balancing Rapid Delivery with Enterprise Standards
You will face the tension of needing to ship bespoke solutions quickly while adhering to Google's high standards for testability, efficiency, and security. Prepare by practicing how to document technical trade-offs and explaining how you prioritize code quality when working under tight 10-12 week internship deadlines.
Managing Complex State in Multi-Agent Systems
Implementing multi-agent orchestration (e.g., LangGraph, CrewAI) introduces significant state-management and debugging difficulties. Prepare by studying how to implement granular tracing and monitoring for LLM-native metrics to ensure you can effectively debug non-deterministic AI behaviors in a production environment.

Practice your answers

Technical practice

Designing Robust Data Pipelines for AI

What to coverExplain how you would design a data pipeline to feed a RAG system, ensuring data freshness and quality. Discuss how you handle schema evolution and potential bottlenecks when integrating with live enterprise infrastructure.

State Management in Multi-Agent Systems

What to coverWhen using frameworks like LangGraph or CrewAI, how do you manage state across multiple agent turns? Describe the trade-offs between centralized state stores and distributed memory patterns in a production environment.

LLM-Native Metrics and Performance Optimization

What to coverBeyond standard latency, what specific metrics would you track for an LLM-based application? Explain how you would optimize for tokens-per-second and cost-per-request without sacrificing output quality.

Integrating APIs with Enterprise Infrastructure

What to coverDescribe the process of building secure connective tissue between a cloud-based AI service and a customer's legacy backend. Focus on authentication, rate limiting, and ensuring the integration is resilient to network failures.

Granular Tracing for Debugging Agentic Workflows

What to coverWhy is granular tracing critical for debugging non-deterministic AI agents? Suggest how you would implement observability to identify where an agentic chain fails or produces hallucinations.

Transitioning Prototypes to Production

What to coverWhat are the key differences between a rapid AI prototype and a production-grade solution? Discuss considerations for security, scalability, and maintainability when moving code into a customer environment.

Programming Language Selection for Backend Systems

What to coverGiven the choice between Python, Go, or Rust for a backend component of an AI system, how do you decide which to use? Address performance, developer velocity, and ecosystem support for ML libraries.

Handling Unstructured Data in Cloud Databases

What to coverHow do you approach indexing and retrieving unstructured data from cloud databases for use in AI workflows? Discuss the role of vector embeddings and metadata filtering in improving retrieval accuracy.

Behavioural practice

Managing Ambiguity in Customer Environments

What to coverDescribe a time you had to solve a technical problem where the requirements were unclear or the environment was unfamiliar. How did you gather the necessary information and align with stakeholders to deliver a solution?

Providing Feedback to Product Teams

What to coverGive an example of when you identified a recurring technical barrier or product limitation. How did you document and communicate this feedback to ensure it could influence future development?

Collaborating with Cross-Functional Teams

What to coverTell me about a time you worked with a team that had different technical priorities than your own. How did you bridge the gap to ensure the project met both customer needs and technical best practices?

Taking Ownership of Full-Stack Roadblocks

What to coverDescribe a situation where you encountered a significant technical roadblock that was outside your immediate area of expertise. What steps did you take to overcome it and what was the final outcome?

Sharpen your knowledge

Topics to prepare

Agentic Workflow Architecture
Focus on the transition from simple LLM prompts to multi-agent systems. Study how to design connective tissue between AI models and enterprise infrastructure, specifically focusing on state management and error handling in agentic loops. Exercise: Design a flowchart for a multi-agent system that handles a customer support ticket, identifying where state must be persisted and how to manage hand-offs between agents.
LLM-Native Performance Metrics
Understand the operational metrics critical for production-grade AI, such as tokens per second, latency, cost-per-request, and accuracy benchmarks. Exercise: Create a dashboard mock-up that tracks these metrics for a RAG-based application, explaining how you would use this data to provide feedback to a product engineering team.

Skills to revise

Multi-Agent Orchestration Frameworks
Gain practical experience with frameworks like LangGraph or CrewAI to build modular, agentic workflows. Exercise: Build a small prototype using LangGraph that implements a 'supervisor' agent to route tasks to specialized worker agents, ensuring you implement granular tracing to monitor the execution path.
Data Pipeline Integration
Develop skills in building robust pipelines that feed structured and unstructured data into AI workflows. Exercise: Write a Python script that extracts data from a mock cloud database, processes it through a cleaning pipeline, and prepares it for ingestion into a vector store for a RAG application.
State Management in Distributed Systems
Focus on maintaining consistent state across complex, multi-step AI interactions. Exercise: Implement a state-machine pattern in your code to manage the lifecycle of an AI agent's task, ensuring that the system can recover or resume if a specific step fails during the process.

Your preparation checklist

Build a Multi-Agent Prototype
Develop a project using LangGraph or CrewAI that solves a specific business problem. Focus on state management and granular tracing to demonstrate your ability to move beyond simple prototypes into production-ready agentic workflows.
Document Data Pipeline Integration
Create a technical walkthrough of a data pipeline you have built. Highlight how you handled data readiness, API connectivity, and security perimeters when integrating AI models with external enterprise infrastructure.
Analyze LLM Performance Metrics
Prepare a summary of how you would optimize an AI solution for production. Include specific metrics like tokens per second and cost-per-request, and explain how you would use these to provide feedback to a product roadmap.
Prepare for Technical Communication
Practice explaining complex technical trade-offs to non-technical stakeholders. As a Forward Deployed Engineer, you must be able to lead technical discovery workshops and translate customer challenges into actionable engineering requirements.
Formulate Strategic Questions
Prepare thoughtful questions for the interviewers regarding the balance between rapid prototyping and long-term system maintainability, and how the team captures field insights to influence Google Cloud's core product development.

Availability has not yet been verified. Status can change after a check; confirm on the employer’s website. Report an issue · Our checking policy

KEEP EXPLORING

Similar AI jobs

Related skills and specializations in Singapore. Matched to this role, not your profile.

Explore more jobs

Finding similar opportunities…