About the Role:

Grade Level (for internal use):
11Lead AI Engineer (Agentic Systems)

Role Summary

As the Lead AI Engineer (Agentic Systems), you will help architect and build the organization’s next generation of autonomous AI workflows. This is a multidisciplinary technical role operating at the intersection of Software Engineering, Data Engineering, and Machine Learning Engineering. You will move beyond straightforward "chatbots" to design production-grade Agentic Systems: intelligent applications capable of reasoning, planning, and executing complex tasks autonomously.

Responsibilities

Agentic Systems Architecture & Core Engineering
- Architect & Build Multi-Agent Workflows: Lead the hands-on design and coding of stateful, production-grade agentic systems using Python and orchestration frameworks like LangGraph, CrewAI, or AutoGen.
- Agent-to-Agent (A2A) Communication: Design and implement robust A2A protocols enabling autonomous agents to collaborate, hand off sub-tasks, and negotiate execution paths dynamically within multi-agent environments.




- State Management & Orchestration: Engineer robust control flows for non-deterministic agents; implement complex message passing, memory persistence, and interruptible state handling to support long-running autonomous tasks.
- Tool Interface Design (MCP): Implement and standardize the Model Context Protocol (MCP) to create universal interfaces between agents, data sources, and operational tools, ensuring modularity and scalability.
- Model Integration & Optimization: Utilize proxy services (i.e. LiteLLM) to manage model routing and fallback strategies; optimize context windows and inference costs across proprietary and open-source models.
- Production Deployment: Containerize agentic workloads using Docker and orchestrate deployments on Kubernetes; leverage AWS AgentCore or similar cloud-native services for scalable infrastructure.

Data Engineering & Operational Real-Time Integration
- Build Agent Data Pipelines: Write and

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