Forward Deployed Engineer Ii, Gcc

Google Google · Big Tech · Warsaw, Poland

Forward Deployed Engineer II, GCC role focused on embedding with customers to build, deploy, and productionize bespoke agentic AI solutions using Google Cloud's AI portfolio. Responsibilities include architecting integrations, developing agentic workflows, building evaluation and observability pipelines, and providing feedback to product teams. Requires experience in software development, ML, NLP, generative AI agents, and DevOps, with preferred experience in multi-agent systems and LLM optimization.

What you'd actually do

  1. Lead the Discovery-to-Deployment Journey: Serve as the lead developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, MCP servers) that drive measurable ROI.
  2. Bridge the Enterprise Gap: Architect and code the connective tissue between Google’s AI products and customer's live infrastructure, including APIs, legacy data silos, and security perimeters.
  3. Engineer for Production Excellence: Build high-performance evaluation (Eval) pipelines and observability frameworks to ensure agentic systems meet rigorous requirements for accuracy, safety, and latency.
  4. Act as a Product Catalyst: Identify repeatable field patterns and technical friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.
  5. Upskill and Embed: Co-build with customer engineering teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Skills

Required

  • software development
  • Python
  • Machine Learning
  • Natural Language Processing (NLP)
  • generative AI agents
  • DevOps

Nice to have

  • Master’s or PhD in AI, Computer Science, or a related technical field
  • implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK)
  • complex patterns (e.g., ReAct, self-reflection, hierarchical delegation)
  • LLM-native metrics (e.g., tokens/sec, cost-per-request)
  • optimizing state management
  • granular tracing
  • secure agentic workflows
  • MCP
  • tool-calling
  • OAuth-based authentication
  • Fluent in French, Spanish, Italian or other European languages

What the JD emphasized

  • embedded builder
  • innovator-builder
  • code, debug, and jointly ship bespoke agentic solutions
  • address blockers to production
  • solving the integration complexities, data readiness issues, and state-management challenges
  • dual purpose: providing white glove deployment of complex AI systems and acting as a critical feedback loop
  • transforming real-world field insights into Google Cloud’s future product roadmap
  • lead developer for complex AI applications
  • production-grade agentic workflows
  • architect and code the connective tissue
  • Build high-performance evaluation (Eval) pipelines and observability frameworks
  • identify repeatable field patterns and technical friction points
  • converting them into reusable modules or formal product feature requests

Other signals

  • building bespoke agentic solutions
  • addressing blockers to production
  • transforming real-world field insights into Google Cloud’s future product roadmap
  • lead developer for complex AI applications
  • transitioning from rapid prototypes to production-grade agentic workflows
  • architect and code the connective tissue between Google’s AI products and customer's live infrastructure
  • build high-performance evaluation (Eval) pipelines and observability frameworks
  • identify repeatable field patterns and technical friction points
  • converting them into reusable modules or formal product feature requests