Forward Deployed Engineer, Gen AI, Google Cloud Consulting (Portuguese, Spanish, English)
Summary
Build and deploy enterprise-grade AI agent systems for Google Cloud customers, coding integrations, debugging production issues, and turning field insights into product improvements.
- Serve as a developer for complex 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 customer's 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 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 similar coding languages.
- 2 years of experience in Cloud Consulting.
- 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 Retrieval-Augmented Generation (RAG)-like architectures to power enterprise AI solutions.
- Ability to communicate fluently in Portuguese, Spanish and English to interact with local business partners and customers.
Preferred qualifications:
- Master’s degree or PhD in AI, Computer Science, or a related technical field.
- Experience leading technical discovery sessions.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, agent development kit (ADK)) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
- Knowledge of Large Language Models (LLM)-native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.