Staff Forward Deployed Engineer - GenAI, Google Cloud

Technologies we use

About the project

Your responsibilities

• Serve as the lead developer for AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, model context protocol 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.
• 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 technical friction points in Google’s AI stack, converting them into reusable modules or product feature requests for the Engineering teams.
• Drive engineering excellence by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to uplevel organizational technical capabilities.

Our requirements

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 8 years of experience building and shipping production-grade AI-driven solutions to external or internal customers using Python, Typescript or comparable languages.
• Experience leading technical discovery sessions with business stakeholders and engineering teams to define AI and hardware infrastructure requirements.
• Experience designing and building AI systems on cloud platforms (e.g., Google Cloud Platform (GCP)).
• Experience building pipelines for structured, unstructured data, incorporating vector databases and retrieval-augmented generation (RAG)-like architectures to power enterprise-grade AI solutions.

Optional

• 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 native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.

Benefits