Staff AI Engineer, Strategic Partners, Google Cloud

About the job

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 partners for our customers. Unlike traditional advisory roles, you will function as an innovator-builder, moving beyond high-level architecture to code, debug, and jointly ship bespoke and scalable agentic solutions directly with our partners, for and 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 partners, you will serve a dual purpose: providing white glove deployment of complex AI systems and acting as a critical connector and feedback loop for the partner to Google, transforming real-world field and partner insights into Google Cloud’s future product roadmap. You will serve as the agent engineer bridging the gap between AI prototypes and production-grade reality for our strategic AI partners and their own FDE teams.

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.

Responsibilities

• Serve as a team lead and developer within the strategic AI partner for AI applications, working with partner’s teams to transition from rapid prototypes to production-grade, replicable agentic workflows (multi-agent systems, MCP servers) that drive measurable Return on Investment (ROI).
• Co-build with a strategic AI partner’s own FDE teams to instill Google-grade development best practices, ensuring project success and high end-user adoption.
• Help partners to build their own agentic delivery capabilities to set them up for long-term success, focusing on the ROI at customer engagements ensuring customer activation.
• Build high-performance evaluation pipelines and observability frameworks to ensure partner developed agentic systems meet requirements for accuracy, safety, and latency.
• Identify repeatable partner and field patterns and friction points in Google’s AI stack, converting them into reusable modules or formal product feature requests for the Engineering teams.

Qualifications

Minimum qualifications:

• 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 architecting AI systems on cloud platforms (e.g., Google Cloud Platform).
• Experience building pipelines for structured and unstructured data using both vector databases and RAG-like architectures to power enterprise AI solutions.
• Experience taking production-grade AI-driven solutions from conception to launch for customers.
• Experience leading technical discovery sessions with customers.

Preferred qualifications:

• Master’s degree or PhD in AI, Computer Science, or a related technical field.
• 10 years of experience with software development using Python or similar coding languages.
• Experience in customer facing role, engaging with, and presenting to, technical stakeholders and executive leaders.
• Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex patterns (e.g., ReAct, self-reflection, hierarchical delegation).
• Knowledge of Large Language Model native metrics (e.g., tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.