Requirements Summary

Minimum qualifications: Bachelor’s degree in Computer Science Engineering, or equivalent practical experience. 5 years of experience in a technical project management or a customer-facing role. 3 years of experience with enterprise systems such as customer relationship management (CRM), databases (e.g., Cloud SQL, BigQuery), security (e.g., IAM roles, service accounts) and application development services (e.g., Cloud Run, Cloud Build, Cloud Source Repositories)

Benefits

Full Job Description

Role Overview As an AI Solutions Consultant, you will manage blocker to production, solving integration complexities, data readiness issues, and state-management challenges that prevent AI from reaching enterprise-grade maturity. You will serve as the lead developer for complex AI applications, transitioning from rapid prototypes to production-grade agentic workflows. What You Will Do Serve as the 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 live infrastructure. Why It Might Be a Fit You will identify repeatable field patterns and technical friction points in agents stack, converting them into reusable modules for other agents. You will co-build with field to instill Google-grade development best practices, ensuring long-term project success and high end-user adoption. Requirements Bachelor’s degree in Computer Science Engineering, or equivalent practical experience 5 years of experience in a technical project management or a customer-facing role 3 years of experience with enterprise systems such as customer relationship management (CRM), databases (e.g., Cloud SQL, BigQuery), security (e.g., IAM roles, service accounts) and application development services (e.g., Cloud Run, Cloud Build, Cloud Source Repositories) Benefits Experience taking production-grade AI-driven solutions from conception to launch Experience architecting Gemini Enterprise-based agents on Google Cloud Platform (GCP) Experience setting up catalog tracking, system safeguards, and evaluation frameworks to govern and operationalize an active community-built agent repository Experience implementing multi-agent systems using frameworks Experience building browser-based UIs and lightweight application front-ends (e.g., Streamlit, Gradio, React) Experience with prompt engineering and a deep understanding of how to interact with and guide large language models using Gemini Knowledge of LLM-native metrics (tokens/sec, cost-per-request) and techniques for optimizing state management and granular tracing Proven ability to implement secure agentic workflows incorporating MCP, tool-calling, and OAuth-based authentication

Skills and Technologies