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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Google ADK GenAI Developer - **Company:** Google LLC - **Location:** Parsippany-Troy Hills, NJ, United States - **Experience:** Expert - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Cloud Computing, Cloud Engineering, Databases, Continuous Integration, Graph Database, Python (Programming Language), Neo4j, NoSQL, Cloud Services, Search Technologies, Software Engineering, SQL Databases, Enterprise Application Integration, Google Cloud, Enterprise Software Applications, GitHub Copilot, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Event Driven Architecture, Kubernetes, Low Latency, Machine Learning Operations, Virtual Agents, Restful APIs, Docker, Microservices - **Published:** August 18, 2026 - **Apply:** https://www.dice.com/job-detail/938bea28-08f0-4cb0-ab8c-41950e2ae8e0 ## About the Role We are seeking a highly skilled Google ADK GenAI Developer with 12+ years of software engineering experience and strong expertise in Generative AI, Python, Google Agent Development Kit (ADK), Google Gemini, and Vertex AI. The ideal candidate will have hands-on experience designing and developing AI agents, multi-agent systems, RAG solutions, and enterprise GenAI applications using Google Cloud technologies. This role will focus on building scalable, secure, and production-ready AI solutions integrated with enterprise applications and data sources., * 12+ years of software development / engineering experience. * Strong hands-on Python development experience. * Hands-on experience with Google Agent Development Kit (ADK). * Strong experience with Google Gemini and Vertex AI. * Experience building AI Agents, Agentic AI, and Multi-Agent Systems. * Strong knowledge of Generative AI, LLMs, Prompt Engineering, and RAG. * Experience with function calling, tool calling, and agent orchestration. * Experience with LangChain, LangGraph, MCP, AutoGen, or similar agent frameworks. * Experience with vector databases such as Pinecone, Weaviate, Chroma, or Vertex AI Vector Search. * Strong understanding of REST APIs, microservices, and event-driven architectures. * Hands-on experience with Google Cloud Platform (Google Cloud Platform). * Experience with Docker, Kubernetes, CI/CD, and cloud-native development. * Experience with SQL/NoSQL databases and enterprise integration patterns. Preferred Skills * Experience with AgentOps, LLM evaluation, AI observability, and monitoring. * Knowledge of MLOps and AI model deployment. * Experience with AI governance, responsible AI, security, and compliance. * Google Cloud certifications. * Experience working in Banking, Financial Services, Healthcare, or large enterprise environments. * Knowledge of Neo4j, graph databases, and knowledge graphs. * Experience with multimodal AI and AI-driven workflow automation. Nice to Have * Experience with Devin AI, Claude Code, GitHub Copilot, or similar AI-assisted development tools. * Experience with advanced AI reasoning and agent orchestration frameworks. * Experience building enterprise-grade AI automation solutions. ## Description * Design, develop, and deploy AI agents and multi-agent systems using Google ADK. * Build enterprise GenAI applications using Google Gemini, Vertex AI, Python, and LLM technologies. * Develop agent workflows, tool integrations, function calling, and autonomous decision-making capabilities. * Design and implement RAG architectures using vector databases and enterprise knowledge sources. * Integrate AI agents with REST APIs, databases, enterprise applications, and cloud services. * Develop scalable microservices and cloud-native AI solutions. * Implement AI governance, security, observability, monitoring, and responsible AI practices. * Optimize LLM and agent performance for latency, scalability, reliability, and cost efficiency. * Collaborate with architects, product owners, data engineers, and business stakeholders. * Lead technical design discussions and mentor development teams on GenAI and agentic AI best practices. ## Related Videos - [Putting the Graph In GraphQL With The Neo4j GraphQL Library](https://www.wearedevelopers.com/videos/257-putting-the-graph-in-graphql-with-the-neo4j-graphql-library) - [Leveraging Real time data in FSIs](https://www.wearedevelopers.com/videos/806-leveraging-real-time-data-in-fsis) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Designing and Deploying Distributed Multimodal Multi-Agent Systems with Google's AI Stac](https://www.wearedevelopers.com/videos/1976-designing-and-deploying-distributed-multimodal-multi-agent-systems-with-google-s-ai-stac) - [Cyber Sleuth: Finding Hidden Connections in Cyber Data](https://www.wearedevelopers.com/videos/893-cyber-sleuth-finding-hidden-connections-in-cyber-data) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Got AI ideas but no money? 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