Forward Deployed Engineer II, GenAI, Google Cloud
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Please submit your resume in English - we can only consider applications submitted in this language.
Only applications of candidates with Mexican citizenship will be evaluated for this role in compliance with the provisions of Article 7 of the Federal Labor Law.
Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 3 years of experience in Python and relevant machine learning packages (e.g., Keras, PyTorch, HF Transformers).
- Experience in applied AI, with a focus on building systems around pretrained models (e.g., prompt engineering, fine-tuning, Retrieval-augmented generation (RAG), 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 Agent Development Kit (ADK)) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of (Large Language Model) LLM-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
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. Unlike traditional advisory roles, 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.
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 code the "connective tissue" between Google’s AI products and customers' 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 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 the Engineering teams.
- Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.
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