Google Agentspace Developer

Job Summary We are seeking an experienced Google Agentspace Developer to design, build, deploy, and maintain enterprise-grade AI agents using Google Cloud’s Agentspace ecosystem. This role will focus on developing intelligent agent solutions, integrating conversational AI capabilities, connecting enterprise data sources, and implementing secure, scalable agent architectures that support business automation and knowledge retrieval use cases. The ideal candidate will have hands-on experience building and deploying agents within Google Agentspace and possess a strong understanding of AI-powered workflows, retrieval-augmented generation (RAG) patterns, API integrations, and Google Cloud AI services. Key Responsibilities AI Agent Development & ConfigurationDesign, build, and configure intelligent agents using Google Agentspace and related Google Cloud AI services. Create and manage agent configurations, data stores, tools, actions, and workflows through the Agent Builder environment. Test, validate, and optimize agent performance using available development and testing environments. Develop scalable agent solutions aligned with business requirements and user experience objectives. Conversational AI & Dialog ManagementIntegrate conversational AI capabilities using enterprise chatbot and virtual assistant frameworks. Design and maintain conversation flows, intents, entities, and session management logic to support seamless user interactions. Enhance conversational experiences by optimizing dialogue structures, response handling, and user engagement patterns. Support multi-turn conversations and contextual interactions across business use cases. Data Integration & Knowledge GroundingConnect AI agents to enterprise data repositories and cloud-based data platforms. Implement retrieval and grounding mechanisms that enable agents to access accurate and contextually relevant information. Integrate structured and unstructured data sources to support intelligent search and knowledge retrieval capabilities. Develop and maintain integrations with databases, cloud storage platforms, search services, and external APIs. Tool Integration & Function CallingDesign and implement custom tools, actions, and API integrations that extend agent capabilities. Develop and manage API specifications and service integrations to support business workflows. Configure and optimize function-calling capabilities, including support for multiple tool executions and orchestration logic. Ensure reliable interaction between AI agents and external systems. Deployment & Lifecycle ManagementDeploy, publish, and maintain production-ready AI agents across enterprise environments. Configure webhooks, endpoints, and integration services required for agent operations. Implement version control, release management, and deployment strategies for agent solutions. Support ongoing maintenance, enhancements, and performance optimization initiatives. AI Platform & Model IntegrationLeverage Google Cloud AI services and foundation models to develop advanced AI-powered solutions. Implement retrieval-augmented generation (RAG) architectures using embeddings, vector search, and knowledge grounding techniques. Integrate large language models and generative AI services into agent workflows. Evaluate and apply emerging AI capabilities to enhance agent functionality and business value. Security & GovernanceImplement secure authentication, authorization, and access control mechanisms. Configure service accounts, permissions, and identity management controls in accordance with organizational security standards. Ensure compliance with data governance, privacy, and security requirements. Monitor and maintain secure access to data sources and integrated systems. Collaboration & Stakeholder EngagementWork closely with business stakeholders, product owners, architects, and development teams to define agent requirements and use cases. Provide technical guidance and recommendations on AI agent design, deployment, and optimization. Support testing, troubleshooting, and issue resolution activities across development and production environments. Contribute to documentation, knowledge sharing, and continuous improvement initiatives. Qualifications RequiredHands-on experience developing and deploying solutions using Google Agentspace. Minimum 8–12 months of practical experience building AI agents in production or enterprise environments. Experience configuring and managing agents, data stores, tools, actions, and workflows within Google Agentspace. Knowledge of conversational AI concepts, including intents, entities, dialogue management, and session handling. Experience integrating enterprise data sources, APIs, and cloud-based platforms into AI solutions. Understanding of retrieval-augmented generation (RAG), embeddings, and knowledge-grounding techniques. Experience implementing API integrations, custom tools, and function-calling capabilities. Knowledge of Google Cloud services r