As a Generative Artificial Intelligence (Gen AI) Forward Deployed Developer at Google Cloud, you will be an embedded developer who bridges the gap between artificial intelligence (AI) products and production-grade reality within customer environments. Unlike advisory roles, you will function as a developer-consultant, moving beyond architecture to code, debug, and jointly ship agentic solutions. You will solve integration, data readiness, and state-management issues that prevent AI from reaching enterprise-grade maturity. By embedding with accounts, you will serve a dual purpose: providing deployment and acting as a feedback loop for 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.
The Canada base salary range for this full-time position is CAD 244,000-250,000 + bonus + equity + benefits. Our salary ranges are determined by role, level, and location. Within the range, individual pay is determined by work location and additional factors, including job-related skills, experience, and relevant education or training.
Please note that the compensation details listed in Canada role postings reflect the base salary only, and do not include bonus, equity, or benefits. Learn more about benefits at Google.
Responsibilities
- Lead the discovery-to-deployment journey, serve as a manager for a team of developers for AI applications, and transition from prototypes to production-grade agentic workflows.
- Bridge the enterprise gap by coding the connective tissue between Google’s AI products and customers' live infrastructure, including application programming interfaces, legacy data silos, and security perimeters.
- Build evaluation pipelines and observability frameworks to ensure agentic systems meet requirements for accuracy, safety, and latency.
- Act as a product catalyst, and identify repeatable field patterns and technical friction points in Google’s AI stack to convert them into reusable modules or product feature requests for the development teams.
- Develop regional leadership and upskilling by mentoring talent, co-building with customer teams, and influencing cross-functional strategies to improve organizational technical capabilities.
Qualifications
Minimum qualifications:
- Bachelor's degree in Science, Technology, Engineering, Mathematics, or equivalent practical experience.
- 10 years of experience in solution development or architecture and 5 years of experience in stakeholder management, professional services, or technical consulting.
- 5 years of experience managing a technical team in a cloud computing environment or customer-facing role.
- 3 years of experience in system architecture and reading code (e.g., Python).
- Experience developing AI systems on cloud platforms.
- Experience in Python programming.
Preferred qualifications:
- Master's degree in Engineering, Computer Science, or related technical fields.
- Experience in consulting, technical consulting in a customer-facing role, with solid results working with a variety of different types of customers, engineers, and business.
- Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, or Google’s ADK) and patterns like ReAct, self-reflection, and hierarchical delegation.
- Knowledge of LLM-native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing.
- Ability to implement secure agentic workflows incorporating Model Context Protocol (MCP), tool-calling, and Open Authorization (OAuth)-based authentication.