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.

• 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.
• Collaborate across Ecosystems: Work alongside product managers and core AI researchers to optimize the implementation of Gemini models ($Gemini 2.5 Flash$, Pro, etc.) within agentic frameworks.

• Bachelor’s degree in Computer Science, a related technical field, or equivalent practical experience.
• 2+ years of experience building and deploying production-grade LLM applications or Agentic AI systems.
• Strong proficiency in at least one primary language supported by ADK: Python (e.g., managing virtual environments using uv or pip) or TypeScript/Node.js.
• Experience with the Google Cloud Platform (GCP) ecosystem, including Cloud Run, Vertex AI, Secret Manager, and Cloud Storage.

• 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.
• Experience implementing rigorous CI/CD pipelines for AI applications (e.g., GitHub Actions, Terraform).
• Strong background in evaluation frameworks for AI agents to benchmark precision, recall, and tool-calling accuracy.