Forward Deployed Engineer I, GenAI, Google Cloud
Google · Greater São Paulo Area
- On site
- Full-time
- $150,000 / year
- Greater São Paulo Area
Job highlights
- Build production-grade AI solutions for customers.
- Integrate AI products with customer infrastructure.
- Develop evaluation and observability frameworks.
- Translate field insights into product features.
- Collaborate with customer engineering teams.
About the role
About the Job
As a Generative AI Forward Deployed Engineer (FDE) at Google Cloud, you will be an embedded builder who bridges the gap between frontier AI products and production-grade reality within customer environments. You will function as an innovator-builder, moving beyond high-level architecture to code, debug, and jointly ship bespoke agentic solutions directly within the customer’s environment. You will address blockers to production, including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity and by embedding with accounts, you will serve a dual purpose, providing white-glove deployment of AI systems and acting as a critical feedback loop, transforming real-world field insights into Google Cloud’s future product roadmap.Google Cloud accelerates every organization’s ability to digitally transform its business and industry. We deliver enterprise-grade solutions that leverage Google’s cutting-edge technology, and tools that help developers build more sustainably. Customers in more than 200 countries and territories turn to Google Cloud as their trusted partner to enable growth and solve their most critical business problems.
Responsibilities
- Serve as a developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, Model Context Protocol (MCP) servers) that drive measurable Return on Investment (ROI).
- Architect and engineer the "connective tissue" linking Google’s AI products to customers' live infrastructure, including Application Programming Interface (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 requirements for accuracy, safety, and latency.
- Identify recurring field patterns and friction points across Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
- Collaborate 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 Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- Experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, TypeScript, or comparable languages.
- Experience in applied AI, with a focus on building systems around pretrained models (e.g., prompt engineering, fine-tuning, Retrieval-augmented generation (RAG), and orchestrating model interactions with external tools to deliver solutions).
- Experience architecting, deploying, or managing solutions on a Cloud Platform (e.g., Google Cloud Platform).
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, or Google’s ADK) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of "LLM-native" metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
Key skills/competency
- Generative AI
- Google Cloud Platform
- Python
- TypeScript
- Applied AI
- Prompt Engineering
- Fine-tuning
- Retrieval-Augmented Generation (RAG)
- Agentic Workflows
- Cloud Solutions
Skills & topics
- Forward Deployed Engineer
- GenAI
- Google Cloud
- AI Engineer
- Machine Learning Engineer
- Python
- TypeScript
- Cloud Computing
- Generative AI
- Prompt Engineering
- RAG
- Agentic Systems
How to get hired
- Customize your resume: Highlight your experience with AI, Python, TypeScript, and Google Cloud Platform. Emphasize production-grade solutions and applied AI skills.
- Tailor your application: Clearly articulate how your skills and experience align with the Forward Deployed Engineer role and Google's mission.
- Prepare for technical interviews: Brush up on AI concepts, model deployment, cloud architecture, and coding challenges in Python or TypeScript.
- Showcase your problem-solving: Be ready to discuss complex integration challenges and how you've overcome them in previous projects.
- Research Google Cloud: Understand their AI offerings, customer use cases, and company values to demonstrate genuine interest.
Technical preparation
Practice building AI solutions with Python/TypeScript.,Implement RAG and prompt engineering techniques.,Deploy and manage solutions on Google Cloud.,Study multi-agent systems frameworks.
Behavioral questions
Describe a complex integration challenge you solved.,How do you bridge AI prototypes to production?,How do you gather and use field feedback?,Explain your experience with customer collaboration.
Frequently asked questions
- What are the key technical skills required for a Forward Deployed Engineer GenAI at Google Cloud?
- The key technical skills for a Forward Deployed Engineer GenAI at Google Cloud include experience building and shipping production-grade AI-driven solutions using Python or TypeScript. You'll need expertise in applied AI, including prompt engineering, fine-tuning, Retrieval-Augmented Generation (RAG), and orchestrating model interactions. Additionally, experience architecting, deploying, or managing solutions on a cloud platform like Google Cloud Platform is essential.
- What does 'Forward Deployed Engineer' mean in the context of Google Cloud's GenAI roles?
- A Forward Deployed Engineer (FDE) at Google Cloud is an embedded builder who bridges the gap between frontier AI products and production-grade reality within customer environments. You'll move beyond high-level architecture to code, debug, and ship bespoke agentic solutions directly within the customer’s environment, addressing integration complexities and data readiness issues.
- What kind of AI systems will I be working with as a Forward Deployed Engineer GenAI?
- As a Forward Deployed Engineer GenAI, you will be working with cutting-edge AI applications, focusing on developing production-grade agentic workflows. This includes multi-agent systems, Model Context Protocol (MCP) servers, and building systems around pre-trained models using techniques like prompt engineering, fine-tuning, and Retrieval-Augmented Generation (RAG).
- How does this role contribute to Google Cloud's product roadmap?
- This role acts as a critical feedback loop. By embedding with customer accounts and deploying AI systems, you'll gain real-world insights into field patterns and friction points. These insights are then transformed into reusable modules or formal product feature requests for Google Cloud's Engineering teams, directly influencing the future product roadmap.
- What is the expected educational background for a Forward Deployed Engineer GenAI at Google Cloud?
- The minimum educational qualification is a Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience. Preferred qualifications include a Master’s degree or PhD in AI, Computer Science, or a related technical field, along with experience in advanced AI concepts and frameworks.
- What are 'LLM-native' metrics mentioned in the preferred qualifications?
- 'LLM-native' metrics refer to performance indicators specific to Large Language Models (LLMs). Examples include tokens per second (tokens/sec) and cost per request. Optimizing these, along with state management and granular tracing, is key for efficient and cost-effective LLM deployments.