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Lead Agentic AI Developer – Google ADK Workflows
Location: Irving TX – 3 days onsite
Duration: 12 months
Role Summary
This role leads the design, architecture, and operationalization of enterprise-grade agentic AI systems spanning multiple orchestration frameworks — including Google's Agent Development Kit (ADK), LangGraph, CrewAI, and AutoGen — deployed at scale on Google Cloud. Embedded within Clients AI engineering practice, the Lead Agentic AI Developer serves as the technical authority for agentic workflow design, framework selection, and cross-agent communication standards using MCP and A2A protocols. In the first 90 days, you will own the delivery of a production multi-agent system fully instrumented with evaluation, observability, and human-in-the-loop controls, integrated with enterprise data and API ecosystems.
Key Responsibilities
- Architect and lead delivery of production-grade multi-agent systems using Google ADK 2.0+, LangGraph, CrewAI, and AutoGen — selecting the right framework per workload based on compliance, auditability, and performance requirements.
- Design complex agentic workflows including branching logic, conditional execution, loop-based self-correction, and parallel fan-out patterns across Google Cloud and enterprise environments.
- Establish and enforce inter-agent communication standards using MCP (Model Context Protocol) and A2A (Agent-to-Agent) protocols to enable seamless integration of heterogeneous agent ecosystems.
- Define human-in-the-loop controls, autonomy boundaries, and agent guardrail frameworks that ensure safe, compliant, and auditable behavior in regulated enterprise contexts.
- Build and maintain RAG and GraphRAG pipelines grounded in enterprise knowledge — including chunking strategies, hybrid vector search, and long-term agent memory management — to maximize factual accuracy and minimize hallucination.
- Deploy and operate agentic infrastructure on Google Cloud (Vertex AI, Cloud Run, Agent Engine) with high availability, security, and low-latency SLAs at scale.
- Create and industrialize automated evaluation frameworks that measure agent reasoning correctness, tool-use accuracy, latency, and business-outcome metrics across all deployed workflows.
- Mentor engineering teams, drive adoption of agentic best practices, and collaborate with product, AI research, and architecture stakeholders to define the enterprise agentic AI roadmap.
Required Qualifications
- 7+ years of experience in software engineering or AI/ML, with at least 3 years directly architecting and deploying production multi-agent or LLM-based systems at enterprise scale.
- Hands-on expertise across multiple agentic frameworks — including Google ADK, LangGraph, CrewAI, and/or AutoGen — with the ability to evaluate and select frameworks based on technical and business requirements.
- Deep proficiency in Python for production agentic development; strong command of prompt engineering, context management, and agentic design patterns (ReAct, Plan-and-Execute, state graphs).
- Demonstrated experience with Google Cloud Platform (Vertex AI, Cloud Run, BigQuery, Cloud Storage) and integration of agentic systems using MCP and A2A protocols.
- Strong background in RAG system design, vector store integration (Vertex AI Vector Search, Pinecone, Elasticsearch), and context engineering strategies including memory management and prompt compression.
- Bachelor's degree in Computer Science, Engineering, or a related technical field, or equivalent practical experience.
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