Forward Deployed Engineer IV, Generative AI, Google Cloud
Embedded engineer at Google Cloud who works directly with strategic customers to take generative AI from prototype to production — coding agentic workflows (multi-agent systems, MCP servers) on Gemini/Vertex AI, integrating with customer infrastructure, and feeding field insights into Google's AI product roadmap. Core stack: Python, RAG, vector databases, cloud.
- Compensation
- $207K–$300K / yr
- Location
- Pittsburgh, PA, USA
- Work setup
- Not specified
- Seniority
- Not specified
- Employment
- Not specified
- Experience
- 8+ years
- Category
- Ai engineering
- Posted
Skills
Must have
- Bachelor's degree in Engineering, Computer Science, a related field, or equivalent practical experience
- 8 years of experience with software development using Python or similar coding languages
- Experience building pipelines for structured and unstructured data using vector databases and RAG-like architectures for enterprise AI solutions
- Experience taking production-grade AI-driven solutions from conception to launch for customers
- Experience architecting AI systems on cloud platforms (e.g., GCP)
- Experience leading technical discovery sessions with customers
Nice to have
- 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, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation)
- Knowledge of LLM native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing
Full description
As a Generative AI (GenAI) Forward Deployed Engineer (FDE) at Google Cloud, you are an embedded builder who bridges the gap between frontier AI products and production-grade reality within customers. 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. This role is designed for high-agency engineers with a founder’s mindset. You will address blockers to production including solving the integration complexities, data readiness issues, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with strategic accounts, you will serve a 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.
It's an exciting time to join Google Cloud’s Go-To-Market team, leading the AI revolution for businesses worldwide. You’ll excel by leveraging Google's brand credibility—a legacy built on inventing foundational technologies and proven at scale. We’ll provide you with the world's most advanced AI portfolio, including frontier Gemini models, and the complete Vertex AI platform, helping you to solve business problems. We’re a collaborative culture providing direct access to DeepMind's engineering and research minds, empowering you to solve customer challenges. Join us to be the catalyst for our mission, drive customer success, and define the new cloud era—the market is yours.
Individual pay is determined by factors including job-related skills, experience, and relevant education or training.
US: $207000 - $300000 (USD) + 20% bonus target + equity + benefits
Learn more about benefits at Google.
- Serve as a developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows (e.g., multi-agent systems, 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 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 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.