As an Agentic AI Engineer specializing in Google’s Agent Development Kit (ADK), you will design, build, and scale production-ready Multi-Agent Systems (MAS) and complex AI workflows. You will bridge the gap between simple LLM prompting and robust, deterministic, enterprise-scale software engineering.
In this role, you will leverage ADK to orchestrate specialized micro-agents, build reliable graph-based workflows, and integrate AI agents seamlessly with enterprise datastores, APIs, and Model Context Protocol (MCP) tools. You will be responsible for moving AI from conceptual prototypes to high-throughput, mission-critical business systems deployed on Agent Engine.
As an Agentic AI Engineer specializing in Google’s Agent Development Kit (ADK), you will design, build, and scale production-ready Multi-Agent Systems (MAS) and complex AI workflows. You will bridge the gap between simple LLM prompting and robust, deterministic, enterprise-scale software engineering.
In this role, you will leverage ADK to orchestrate specialized micro-agents, build reliable graph-based workflows, and integrate AI agents seamlessly with enterprise datastores, APIs, and Model Context Protocol (MCP) tools. You will be responsible for moving AI from conceptual prototypes to high-throughput, mission-critical business systems deployed on Agent Engine.
As an Agentic AI Engineer specializing in Google’s Agent Development Kit (ADK), you will design, build, and scale production-ready Multi-Agent Systems (MAS) and complex AI workflows. You will bridge the gap between simple LLM prompting and robust, deterministic, enterprise-scale software engineering.
In this role, you will leverage ADK to orchestrate specialized micro-agents, build reliable graph-based workflows, and integrate AI agents seamlessly with enterprise datastores, APIs, and Model Context Protocol (MCP) tools. You will be responsible for moving AI from conceptual prototypes to high-throughput, mission-critical business systems deployed on Agent Engine.
What you will be doing:
- Architect Multi-Agent Systems: Design and implement structured multi-agent architectures (Sequential Pipelines, Parallel Fan-out/Gather, and Loop-based self-correction) using Google ADK.
- Develop Core Agentic Logic: Build deterministic graph workflows that effectively weave adaptive AI reasoning with explicit execution paths to ensure predictable outcomes.
- Tool & Skill Integration: Create, map, and integrate custom Agent Skills and third-party tools (including Google Maps MCP, Search tools, and custom enterprise APIs).
- Evaluation & Debugging: Use ADK evaluation tools to test execution trajectories, manage loop limits, avoid key collisions, and drastically mitigate production hallucinations.
- Scale and Deploy: Deploy optimized agents to Agent Engine (via Cloud Run / Google Cloud Platform) and maintain high availability, security, and low latency.
What skills and experience you will bring:
- Bachelor’s degree in computer science, Software Engineering, Artificial Intelligence, Machine Learning, or a related technical discipline; equivalent practical experience will also be considered.
- 2+ years of hands-on experience designing, developing, and deploying production-grade Generative AI, Large Language Model (LLM), or Agentic AI applications in enterprise environments.
- Strong software engineering proficiency in Python and/or TypeScript/Node.js, including modern development practices, dependency management, testing frameworks, API development, and CI/CD pipelines.
- Experience building autonomous AI agents, intelligent workflows, retrieval-augmented generation (RAG) solutions, or multi-step LLM applications that interact with external systems and data sources.
- Hands-on experience with Google Cloud Platform (GCP), including services such as Vertex AI, Cloud Run, Cloud Storage, Secret Manager, IAM, and cloud-native application deployment patterns.
- Solid understanding of prompt engineering, tool calling, function execution, agent memory management, context optimization, and LLM application architecture.
- Experience integrating AI solutions with enterprise APIs, databases, knowledge repositories, and third-party platforms while maintaining security, scalability, and observability standards.
- Strong troubleshooting and debugging skills with the ability to monitor, optimize, and improve AI application performance in production environments.
Preferred qualifications:
- Hands-on experience developing with the official open-source Google Agent Development Kit (ADK 2.0+).
- Deep understanding of multi-agent orchestration patterns, state graph architectures, and deterministic routing.
- Familiarity with Model Context Protocol (MCP) and integrating external tools seamlessly into LLM context windows.