Staff Forward Deployed Engineer, Google Cloud Consulting (multiple Languages)

Google Google · Big Tech · Dublin, Ireland

Staff Forward Deployed Engineer for Google Cloud Consulting, focused on building and deploying bespoke agentic AI solutions within customer environments. This role bridges frontier AI products with production reality, addressing integration, data readiness, and state-management issues. Responsibilities include developing agentic workflows, architecting connective tissue, building evaluation pipelines, and providing feedback to product teams. Requires strong software development skills, experience with RAG and vector databases, and cloud AI platforms.

What you'd actually do

  1. 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).
  2. Architect and code the "connective tissue" between Google’s AI products and customer's live infrastructure, including Application Programming Interfaces (APIs), legacy data silos, and security perimeters as part of an expert team.
  3. Build high-performance evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
  4. 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.
  5. Co-build with pre-sales and product teams to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption.

Skills

Required

  • software development using Python or similar coding languages
  • building pipelines for structured and unstructured data
  • vector databases
  • Retrieval-Augmented Generation (RAG)-like architectures
  • power enterprise AI solutions
  • taking production-grade AI solutions from conception to launch
  • architecting AI systems on cloud platforms (e.g., Google Cloud Platform (GCP))
  • leading technical discovery sessions with customers
  • communicate in French, German or other European language (e.g., Spanish and Italian) fluently

Nice to have

  • implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, Agent Development Kit (ADK))
  • complex patterns (e.g., ReAct, self-reflection, hierarchical delegation)
  • Knowledge of "Large Language Model (LLM)-native" metrics (e.g., tokens/sec, cost-per-request)
  • techniques for optimizing state management and granular tracing
  • Ability to travel regionally to engage with local stakeholders and customers

What the JD emphasized

  • production-grade reality
  • code, debug, and jointly ship bespoke agentic solutions
  • address blockers to production
  • integration complexities
  • data readiness issues
  • state-management issues
  • AI from reaching enterprise-grade maturity
  • white glove deployment of complex Artificial Intelligence (AI) systems
  • critical feedback loop
  • transforming real-world field insights into Google Cloud’s future product roadmap
  • production-grade agentic workflows
  • multi-agent systems
  • Model Context Protocol (MCP) servers
  • architect and code the "connective tissue"
  • customer's live infrastructure
  • Application Programming Interfaces (APIs)
  • legacy data silos
  • security perimeters
  • high-performance evaluation pipelines
  • observability frameworks
  • agentic systems meet requirements for accuracy, safety, and latency
  • repeatable field patterns and friction points
  • reusable modules
  • formal product feature requests
  • Google-grade development best practices
  • long-term project success
  • high end-user adoption
  • production-grade AI solutions
  • architecting AI systems on cloud platforms
  • technical discovery sessions with customers
  • multi-agent systems
  • complex patterns
  • LLM-native" metrics
  • state management
  • granular tracing

Other signals

  • building bespoke agentic solutions
  • addressing blockers to production
  • feedback loop to product roadmap
  • deploying complex AI systems
  • leveraging frontier Gemini models
  • Vertex AI platform